init: #13: 1: [TCWG CI] https://ci.linaro.org/job/tcwg_bmk-code_speed-cpu2017rate--llvm-arm-master-O2-build/13/

Results :
  | # reset_artifacts:
  | -10
  | # build_bmk_llvm:
  | -3
  | # benchmark -- -O2_marm:
  | 1

check_regression status : 0

7 files changed, 1396 insertions, 0 deletions

diff --git a/notify/extra-bisect-params b/notify/extra-bisect-params
new file mode 100644
index 0000000..fa6c7c9
--- /dev/null
+++ b/notify/extra-bisect-params
@@ -0,0 +1 @@
+extra_build_params=
diff --git a/notify/jira/comment-template.txt b/notify/jira/comment-template.txt
new file mode 100644
index 0000000..d57a077
--- /dev/null
+++ b/notify/jira/comment-template.txt
@@ -0,0 +1,3 @@
+[LLVM-651]
+No change
+Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-cpu2017rate--llvm-arm-master-O2-build/13/artifact/artifacts/notify/mail-body.txt/*view*/
diff --git a/notify/lnt_report.json b/notify/lnt_report.json
new file mode 100644
index 0000000..ce3a38a
--- /dev/null
+++ b/notify/lnt_report.json
@@ -0,0 +1,95 @@
+{
+    "Machine": {
+        "Info": {},
+        "Name": "llvm-arm-master-O2"
+    },
+    "Run": {
+        "Info": {
+            "__report_version__": "1",
+            "run_order": "llvmorg-17-init-07454-g7a1044c6affe",
+            "tag": "tcwg_bmk-code_speed-cpu2017rate"
+        },
+        "Start Time": "2023-11-22 23:00:40"
+    },
+    "Tests": [
+        {
+            "Data": [
+                29232
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.505.mcf_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                3879684
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.523.xalancbmk_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                91600
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.531.deepsjeng_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                13056
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.519.lbm_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                1719176
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.538.imagick_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                178055
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.557.xz_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                135845
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.541.leela_r.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                10547
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.531.deepsjeng_r.exec"
+        }
+	   ,
+        {
+            "Data": [
+                14075
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.505.mcf_r.exec"
+        }
+	   ,
+        {
+            "Data": [
+                10456
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk-code_speed-cpu2017rate.557.xz_r.exec"
+        }
+    ]
+}
diff --git a/notify/mail-body.txt b/notify/mail-body.txt
new file mode 100644
index 0000000..95d4308
--- /dev/null
+++ b/notify/mail-body.txt
@@ -0,0 +1,27 @@
+Dear contributor, our automatic CI has detected problems related to your patch(es).  Please find some details below.  If you have any questions, please follow up on linaro-toolchain@lists.linaro.org mailing list, Libera's #linaro-tcwg channel, or ping your favourite Linaro toolchain developer on the usual project channel.
+
+In CI config tcwg_bmk-code_speed-cpu2017rate/llvm-arm-master-O2 after:
+
+  | baseline build
+
+No change
+
+The configuration of this build is:
+Below reproducer instructions can be used to re-build both "first_bad" and "last_good" cross-toolchains used in this bisection.  Naturally, the scripts will fail when triggerring benchmarking jobs if you don\'t have access to Linaro TCWG CI.
+
+Configuration:
+- Benchmark: SPEC CPU2017
+- Toolchain: Clang + Glibc + LLVM Linker
+- Version: all components were built from their tip of trunk
+- Target: arm-linux-gnueabihf
+- Compiler flags: O2
+- Hardware: NVidia TK1 4x Cortex-A15
+
+This benchmarking CI is work-in-progress, and we welcome feedback and suggestions at linaro-toolchain@lists.linaro.org .  In our improvement plans is to add support for SPEC CPU2017 benchmarks and provide "perf report/annotate" data behind these reports.
+
+-----------------8<--------------------------8<--------------------------8<--------------------------
+The information below can be used to reproduce a debug environment:
+
+Current build   : https://ci.linaro.org/job/tcwg_bmk-code_speed-cpu2017rate--llvm-arm-master-O2-build/13/artifact/artifacts
+Reference build : artifact/artifacts
+
diff --git a/notify/mail-recipients.txt b/notify/mail-recipients.txt
new file mode 100644
index 0000000..aa219ef
--- /dev/null
+++ b/notify/mail-recipients.txt
@@ -0,0 +1 @@
+bcc:tcwg-validation@linaro.org
diff --git a/notify/mail-subject.txt b/notify/mail-subject.txt
new file mode 100644
index 0000000..704fbbe
--- /dev/null
+++ b/notify/mail-subject.txt
@@ -0,0 +1 @@
+[Linaro-TCWG-CI] baseline build: No change on arm O2
diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log
new file mode 100644
index 0000000..2141b32
--- /dev/null
+++ b/notify/output-bmk-results.log
@@ -0,0 +1,1268 @@
+ --- modulename: output-bmk-results, funcname: <module>
+<string>(1):  --- modulename: output-bmk-results, funcname: main
+output-bmk-results.py(322):     results_csv = sys.argv[1]
+output-bmk-results.py(323):     variability_file = sys.argv[2]
+output-bmk-results.py(324):     run_step_artifacts_dir = sys.argv[3]
+output-bmk-results.py(325):     metric = sys.argv[4]
+output-bmk-results.py(326):     mode = sys.argv[5]
+output-bmk-results.py(327):     details = sys.argv[6]
+output-bmk-results.py(329):     merged_df = read_results_csv(results_csv)
+ --- modulename: output-bmk-results, funcname: read_results_csv
+output-bmk-results.py(312):     df = pd.read_csv(results_csv)
+output-bmk-results.py(313):     df = df.fillna(-1)
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+ --- modulename: output-bmk-results, funcname: get_comparable_metrics
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(207):            & metric_utils.comparable_metrics
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(319):     return df
+output-bmk-results.py(330):     read_specific_variability_file(variability_file)
+ --- modulename: output-bmk-results, funcname: read_specific_variability_file
+output-bmk-results.py(51):     if not os.path.exists(bmk_specific_filename):
+output-bmk-results.py(52):        return None
+output-bmk-results.py(331):     output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results
+output-bmk-results.py(278):     f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w")
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(279):     f_ebp  = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w")
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(280):     f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(282):     f_ebp.write("extra_build_params=")
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36):     if not self.predicate or not self.outf:
+output-bmk-results.py(38):     self.outf.write(string)
+output-bmk-results.py(286):     df = merged_df[merged_df["benchmark"] != "Mean"]
+output-bmk-results.py(289):     exe_df = df[df["symbol"].str.endswith("_base.default")]
+output-bmk-results.py(290):     sym_df = df[~df["symbol"].str.endswith("_base.default")]
+output-bmk-results.py(293):     output_bmk_results_status(exe_df, "regression", f_regr, f_ebp, run_step_artifacts, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_status
+output-bmk-results.py(256):     f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(258):     print(results_df)
+          benchmark                    symbol  ...       status_x       status_y
+0   500.perlbench_r  perlbench_r_base.default  ...  failed-to-run  failed-to-run
+1         502.gcc_r     cpugcc_r_base.default  ...  failed-to-run  failed-to-run
+2         505.mcf_r        mcf_r_base.default  ...        success        success
+7        508.namd_r       namd_r_base.default  ...  failed-to-run  failed-to-run
+8      510.parest_r     parest_r_base.default  ...  failed-to-run  failed-to-run
+9      511.povray_r     povray_r_base.default  ...  failed-to-run  failed-to-run
+10        519.lbm_r        lbm_r_base.default  ...  failed-to-run  failed-to-run
+11    520.omnetpp_r    omnetpp_r_base.default  ...  failed-to-run  failed-to-run
+12  523.xalancbmk_r   cpuxalan_r_base.default  ...  failed-to-run  failed-to-run
+13       525.x264_r       x264_r_base.default  ...  failed-to-run  failed-to-run
+14    526.blender_r    blender_r_base.default  ...  failed-to-run  failed-to-run
+15  531.deepsjeng_r  deepsjeng_r_base.default  ...        success        success
+20    538.imagick_r    imagick_r_base.default  ...  failed-to-run  failed-to-run
+21      541.leela_r      leela_r_base.default  ...  failed-to-run  failed-to-run
+22        544.nab_r        nab_r_base.default  ...  failed-to-run  failed-to-run
+23         557.xz_r         xz_r_base.default  ...        success        success
+
+[16 rows x 20 columns]
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(275):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(294):     output_bmk_results_status(exe_df, "improvement", None,  None,  run_step_artifacts, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_status
+output-bmk-results.py(256):     f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(258):     print(results_df)
+          benchmark                    symbol  ...       status_x       status_y
+0   500.perlbench_r  perlbench_r_base.default  ...  failed-to-run  failed-to-run
+1         502.gcc_r     cpugcc_r_base.default  ...  failed-to-run  failed-to-run
+2         505.mcf_r        mcf_r_base.default  ...        success        success
+7        508.namd_r       namd_r_base.default  ...  failed-to-run  failed-to-run
+8      510.parest_r     parest_r_base.default  ...  failed-to-run  failed-to-run
+9      511.povray_r     povray_r_base.default  ...  failed-to-run  failed-to-run
+10        519.lbm_r        lbm_r_base.default  ...  failed-to-run  failed-to-run
+11    520.omnetpp_r    omnetpp_r_base.default  ...  failed-to-run  failed-to-run
+12  523.xalancbmk_r   cpuxalan_r_base.default  ...  failed-to-run  failed-to-run
+13       525.x264_r       x264_r_base.default  ...  failed-to-run  failed-to-run
+14    526.blender_r    blender_r_base.default  ...  failed-to-run  failed-to-run
+15  531.deepsjeng_r  deepsjeng_r_base.default  ...        success        success
+20    538.imagick_r    imagick_r_base.default  ...  failed-to-run  failed-to-run
+21      541.leela_r      leela_r_base.default  ...  failed-to-run  failed-to-run
+22        544.nab_r        nab_r_base.default  ...  failed-to-run  failed-to-run
+23         557.xz_r         xz_r_base.default  ...        success        success
+
+[16 rows x 20 columns]
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(275):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(297):     output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_1
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 505.mcf_r,mcf_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 531.deepsjeng_r,deepsjeng_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 557.xz_r,xz_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(298):     output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_1
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 505.mcf_r,mcf_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 531.deepsjeng_r,deepsjeng_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 557.xz_r,xz_r_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(300):     output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_1
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 505.mcf_r,[.] primal_bea_mpp : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 505.mcf_r,[.] price_out_impl : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 505.mcf_r,[.] cost_compare : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 505.mcf_r,[.] replace_weaker_arc : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z15FindFirstRemovePy : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z4makeP7state_ti : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_find : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_skip : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(301):     output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_1
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] primal_bea_mpp : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] price_out_impl : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] cost_compare : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] replace_weaker_arc : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z15FindFirstRemovePy : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z4makeP7state_ti : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_find : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(58):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_skip : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(303):     f_ebp.write("\n")
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36):     if not self.predicate or not self.outf:
+output-bmk-results.py(38):     self.outf.write(string)
+output-bmk-results.py(305):     f_skip.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(306):     f_regr.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(307):     f_ebp.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(332):     return 0