Merge branch 'vara-dev' into f-SynthCompileTimeCS

docs/source/conf.py

@@ -87,6 +87,10 @@
 # modules that require this module before setting the type checking flag.
 import scipy.stats  # isort:skip
 
+# Matplotlib >=3.8 has a type-checking-flag-guarded import of a symbol that does
+# not exist in the shipped version.
+import matplotlib.pyplot  # isort:skip
+
 # The autodocs typehints plugin does not resolve circular imports caused by type
 # annotations, so we have to manually break the circles.
 import rich.console  # isort:skip

docs/source/vara-ts-api/tools/vara-cs-gui.rst

@@ -10,6 +10,7 @@ The gui is started by::
 
 The gui provides 3 Strategies to generate case studies:
     - Manual revision selection: Select revision from the revision history of a project. Multiple revisions can be selected by holding `ctrl` and ranges by holding `shift`. Revisions which are blocked because of bugs in the compilation of the project are marked blue.
+
     .. figure:: vara-cs-gui-manual.png
 
     - Random Sampling: Sample a number of revisions using a random a Normal or HalfNormal Distribution.

tests/utils/test_git_util.py

@@ -2,7 +2,7 @@
 import unittest
 from pathlib import Path
 
-from benchbuild.utils.revision_ranges import RevisionRange
+from benchbuild.utils.revision_ranges import RevisionRange, SingleRevision
 
 from varats.project.project_util import (
     get_local_project_git,
@@ -568,6 +568,17 @@ def test_specification_validity_range_multiple_binaries(self) -> None:
         self.assertIn("SingleLocalMultipleRegions", self.rv_map)
         self.assertIn("SingleLocalSimple", self.rv_map)
 
+    def test_specification_single_revision(self) -> None:
+        """Check if we can add binaries that are only valid with a single
+        revision."""
+        self.rv_map.specify_binary(
+            "build/bin/SingleLocalMultipleRegions",
+            BinaryType.EXECUTABLE,
+            only_valid_in=SingleRevision("162db88346")
+        )
+
+        self.assertIn("SingleLocalMultipleRegions", self.rv_map)
+
     def test_specification_binaries_with_special_name(self) -> None:
         """Check if we can add binaries that have a special name."""
         self.rv_map.specify_binary(

varats-core/varats/utils/git_util.py

@@ -1063,7 +1063,9 @@ def specify_binary(
         override_entry_point = kwargs.get("override_entry_point", None)
         if override_entry_point:
             override_entry_point = Path(override_entry_point)
-        validity_range = kwargs.get("only_valid_in", None)
+        validity_range: AbstractRevisionRange = kwargs.get(
+            "only_valid_in", None
+        )
         valid_exit_codes = kwargs.get("valid_exit_codes", None)
 
         wrapped_binary = ProjectBinaryWrapper(
@@ -1072,6 +1074,7 @@ def specify_binary(
         )
 
         if validity_range:
+            validity_range.init_cache(self.__repo_location)
             self.__revision_specific_mappings[validity_range].append(
                 wrapped_binary
             )
@@ -1087,10 +1090,7 @@ def __getitem__(self,
 
         for validity_range, wrapped_binaries \
                 in self.__revision_specific_mappings.items():
-            if revision in get_all_revisions_between(
-                validity_range.id_start, validity_range.id_end, ShortCommitHash,
-                self.__repo_location
-            ):
+            if revision in map(ShortCommitHash, validity_range):
                 revision_specific_binaries.extend(wrapped_binaries)
 
         revision_specific_binaries.extend(self.__always_valid_mappings)

varats/varats/data/metrics.py

@@ -28,7 +28,7 @@ def gini_coefficient(distribution: pd.Series) -> float:
     Calculates the Gini coefficient of the data.
 
     For more information see online
-    `gini coefficient <https://en.wikipedia.org/wiki/Gini_coefficient>`_.
+    `Gini coefficient <https://en.wikipedia.org/wiki/Gini_coefficient>`_.
 
     Args:
         distribution: sorted series to calculate the Gini coefficient for
@@ -141,10 +141,12 @@ class ConfusionMatrix(tp.Generic[T]):
     """
     Helper class to automatically calculate classification results.
 
-                        |  Predicted Positive (PP)  |  Predicted Negative (PN)
-    --------------------|---------------------------|--------------------------
-    Actual Positive (P) |  True Positive      (TP)  |  False Negative     (FN)
-    Actual Negative (N) |  False Positive     (FP)  |  True Negative      (TN)
+    +---------------------+-------------------------+-------------------------+
+    |                     | Predicted Positive (PP) | Predicted Negative (PN) |
+    +---------------------+-------------------------+-------------------------+
+    | Actual Positive (P) | True Positive      (TP) | False Negative     (FN) |
+    | Actual Negative (N) | False Positive     (FP) | True Negative      (TN) |
+    +---------------------+-------------------------+-------------------------+
 
     Reference: https://en.wikipedia.org/wiki/Precision_and_recall
     """