Merge pull request fga-eps-mds#39 from fga-eps-mds/develop

Sincronização dos repositórios 2023-2

.github/workflows/build.yml

@@ -0,0 +1,20 @@
+name: Build
+on:
+  push:
+    branches:
+      - develop
+  pull_request:
+    types: [opened, synchronize, reopened]
+jobs:
+  sonarcloud:
+    name: SonarCloud
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0 
+      - name: SonarCloud Scan
+        uses: SonarSource/sonarcloud-github-action@master
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}  
+          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}

.github/workflows/metrics.yml

@@ -5,6 +5,7 @@ on:
     branches: [main, develop]
     tags:
       - "v*"
+  workflow_dispatch:
 
 jobs:
   release:
@@ -24,7 +25,7 @@ jobs:
         run: |
           git config --global user.email "${{secrets.USER_EMAIL}}"
           git config --global user.name "${{secrets.USER_NAME}}"
-          git clone --single-branch --branch main "https://x-access-token:${{secrets.API_TOKEN_DOC}}@github.com/fga-eps-mds/2023-1-MeasureSoftGram-Doc" doc
+          git clone --single-branch --branch main "https://x-access-token:${{secrets.API_TOKEN_DOC}}@github.com/fga-eps-mds/2023.2-MeasureSoftGram-DOC" doc
           mkdir -p doc/analytics-raw-data
           cp -R analytics-raw-data/*.json doc/analytics-raw-data
           cd doc

.github/workflows/msgram-analysis.yml

@@ -0,0 +1,20 @@
+name: MSG Action
+on:
+  pull_request:
+    branches: [ develop ]
+    types: [opened, closed, reopened, synchronize]
+  workflow_dispatch:
+jobs:
+  msgram_job:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: 2023.2 Action MeasureSoftGram
+        uses: fga-eps-mds/[email protected]    
+        id: msgram
+        with:
+          githubToken: ${{ secrets.GITHUB_TOKEN }} # Token do GitHub
+          sonarProjectKey: "fga-eps-mds_2023-2-MeasureSoftGram-Core" # (opcional) Chave do projeto no SonarQube
+          msgramServiceToken: ${{ secrets.MSGRAM_SERVICE_TOKEN }} # Token para acessar o serviço MeasureSoftGram
+          productName: "MeasureSoftGram" # Nome do produto

.github/workflows/python-publish.yml

@@ -5,6 +5,7 @@ on:
     branches: [ develop ]
     tags:
       - "v*"
+  workflow_dispatch:
 
 jobs:
   deploy:
@@ -32,5 +33,5 @@ jobs:
         run: python -m twine upload -u __token__ -p ${{ secrets.TEST_PYPI_API_TOKEN }} --repository testpypi dist/*
 
       - name: Publish package on pypi
-        if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+        #if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
         run: python -m twine upload -u __token__ -p ${{ secrets.PYPI_API_TOKEN }} dist/*

README.md

@@ -1,4 +1,4 @@
-# 2023-1-MeasureSoftGram-Core
+# 2023-2 MeasureSoftGram-Core
 
 ## Badges
 

parser.py

@@ -23,7 +23,6 @@
 BASE_URL = "https://sonarcloud.io/api/measures/component_tree?component=fga-eps-mds_"
 
 if __name__ == "__main__":
-
     REPO = sys.argv[1]
     RELEASE_VERSION = sys.argv[2]
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "msgram_core"
-version = "1.3.3"
+version = "1.4.5"
 description = "The MeasureSoftGram-Core is a Software system for continuous quality of product observation and multidimensional use in continuous design engineering software and is where you have the innovative mathematical models for software analysis."
 readme = "README.md"
 authors = [

sonar-project.properties

@@ -1,4 +1,4 @@
-sonar.projectKey=fga-eps-mds_2023-1-MeasureSoftGram-Core
+sonar.projectKey=fga-eps-mds_2023-2-MeasureSoftGram-Core
 sonar.organization=fga-eps-mds-1
 
 sonar.python.version=3

src/core/aggregated_normalized_measures.py

@@ -45,7 +45,9 @@ def non_complex_files_density(
 
     files_in_thresholds_bool_index = complex_files_density <= max_threshold
     files_functions_gt_zero_bool_index = files_functions > 0
-    x = complex_files_density[files_in_thresholds_bool_index * files_functions_gt_zero_bool_index]
+    x = complex_files_density[
+        files_in_thresholds_bool_index * files_functions_gt_zero_bool_index
+    ]
 
     interpretation_function_value = transformations.interpretation_function(
         x=x,
@@ -60,7 +62,9 @@ def non_complex_files_density(
     return aggregated_and_normalized_measure
 
 
-def commented_files_density(data_frame, min_threshold: float = 10, max_threshold: float = 30):
+def commented_files_density(
+    data_frame, min_threshold: float = 10, max_threshold: float = 30
+):
     """
     Calculates commented files density.
 
@@ -104,7 +108,9 @@ def commented_files_density(data_frame, min_threshold: float = 10, max_threshold
     return aggregated_and_normalized_measure
 
 
-def absence_of_duplications(data_frame, min_threshold: float = 0, max_threshold: float = 5.0):
+def absence_of_duplications(
+    data_frame, min_threshold: float = 0, max_threshold: float = 5.0
+):
     """
     Calculates duplicated files absence (em3).
 
@@ -113,7 +119,9 @@ def absence_of_duplications(data_frame, min_threshold: float = 0, max_threshold:
     """
     files_duplicated_lines_density = data_frame["duplicated_lines_density"]  # m5 metric
 
-    Checker.check_metric_values(files_duplicated_lines_density, "duplicated_lines_density")
+    Checker.check_metric_values(
+        files_duplicated_lines_density, "duplicated_lines_density"
+    )
 
     if len(files_duplicated_lines_density) == 0:
         return 0.0
@@ -177,7 +185,9 @@ def test_coverage(
     return aggregated_and_normalized_measure
 
 
-def fast_test_builds(data_frame, min_threshold: float = 0, max_threshold: float = 300000):
+def fast_test_builds(
+    data_frame, min_threshold: float = 0, max_threshold: float = 300000
+):
     """
     Calculates fast test builds (em5)
     This function gets the dataframe metrics
@@ -256,6 +266,88 @@ def passed_tests(data_frame, min_threshold: float = 0, max_threshold: float = 1)
         gain_interpretation=1,
     )
 
-    aggregated_and_normalized_measure = transformations.calculate_measure(interpretation_function_value)
+    aggregated_and_normalized_measure = transformations.calculate_measure(
+        interpretation_function_value
+    )
+
+    return aggregated_and_normalized_measure
+
+
+def team_throughput(
+    data_frame,
+    min_threshold: float = 45,
+    max_threshold: float = 100,
+):
+    """
+    Calculates team throughput (em7).
+
+    This function gets the dataframe metrics
+    and returns the team throughput measure (em7).
+    """
+    total_issues = data_frame["total_issues"]
+    resolved_issues = data_frame["resolved_issues"]
+
+    Checker.check_metric_value(total_issues, "total_issues")
+    Checker.check_metric_value(resolved_issues, "resolved_issues")
+
+    Checker.check_threshold(min_threshold, max_threshold, "team_throughput")
+
+    team_throughput_value = ems_functions.get_team_throughput(
+        data={
+            "total_issues": int(total_issues),
+            "resolved_issues": int(resolved_issues),
+        }
+    )
+
+    interpretation_function_value = transformations.interpretation_function(
+        x=team_throughput_value,
+        min_threshold=min_threshold,
+        max_threshold=max_threshold,
+        gain_interpretation=1,
+    )
+
+    aggregated_and_normalized_measure = transformations.calculate_measure(
+        interpretation_function_value
+    )
+
+    return aggregated_and_normalized_measure
+
+
+def ci_feedback_time(
+    data_frame,
+    min_threshold: float = 1,
+    max_threshold: float = 900,
+):
+    """
+    Calculates CI Feedback Time (em8).
+
+    This function gets the dataframe metrics
+    and returns the CI Feedback Time (em8).
+    """
+    total_builds = data_frame["total_builds"]
+    sum_ci_feedback_times = data_frame["sum_ci_feedback_times"]
+
+    Checker.check_metric_value(total_builds, "total_builds")
+    Checker.check_metric_value(sum_ci_feedback_times, "sum_ci_feedback_times")
+
+    Checker.check_threshold(min_threshold, max_threshold, "ci_feedback_time")
+
+    ci_feedback_time_value = ems_functions.get_ci_feedback_time(
+        data={
+            "total_builds": int(total_builds),
+            "sum_ci_feedback_times": int(sum_ci_feedback_times),
+        }
+    )
+
+    interpretation_function_value = transformations.interpretation_function(
+        x=ci_feedback_time_value,
+        min_threshold=min_threshold,
+        max_threshold=max_threshold,
+        gain_interpretation=1,
+    )
+
+    aggregated_and_normalized_measure = transformations.calculate_measure(
+        interpretation_function_value
+    )
 
     return aggregated_and_normalized_measure

src/core/measures_functions.py

@@ -183,25 +183,29 @@ def get_test_coverage(data: dict):
     return coverage, number_of_files
 
 
-# def calculate_em7(data: dict):
-#     """
-#     Calculates team throughput (em7).
+def get_team_throughput(data: dict[str, int]):
+    """
+    Calculates team throughput (em7)
+
+    This function calculates the team throughput measure (em7)
+    used to assess the testing status sub characteristic.
+    """
+
+    total_issues = data["total_issues"]
+    resolved_issues = data["resolved_issues"]
 
-#     This function calculates the team throughput measure (em7)
-#     used to assess the functional completeness subcharacteristic.
-#     """
-#     resolved_issues_with_us_label = data[
-#         "number_of_resolved_issues_with_US_label_in_the_last_x_days"
-#     ]
+    return 100 * (resolved_issues / total_issues)
 
-#     total_issues_with_us_label = data[
-#         "total_number_of_issues_with_US_label_in_the_last_x_days"
-#     ]
 
-#     x, y = create_coordinate_pair(0, 1, reverse_y=True)
+def get_ci_feedback_time(data: dict[str, int]):
+    """
+    Calculates CI Feedback Time (em8)
+
+    This function calculates the CI Feedback Time measure (em8)
+    used to assess the testing status sub characteristic.
+    """
 
-#     if7 = np.divide(resolved_issues_with_us_label, total_issues_with_us_label)
+    total_builds = data["total_builds"]
+    sum_ci_feedback_times = data["sum_ci_feedback_times"]
 
-#     if np.isnan(if7) or np.isinf(if7):
-#         return 0
-#     return np.interp(if7, x, y)
+    return sum_ci_feedback_times // total_builds