def test_with_flag():
metric = CodeChurn(path_to_repo='test-repos/pydriller',
from_commit='ab36bf45859a210b0eae14e17683f31d19eea041',
to_commit='fdf671856b260aca058e6595a96a7a0fba05454b',
ignore_added_files=True)
code_churns = metric.count()
assert len(code_churns) == 7
assert str(Path('domain/__init__.py')) not in code_churns
assert code_churns[str(Path('domain/commit.py'))] == 0
def test_with_dates(path_to_repo, filepath, since, to, expected_count, expected_max, expected_avg):
metric = CodeChurn(path_to_repo=path_to_repo, since=since, to=to)
actual_count = metric.count()
actual_max = metric.max()
actual_avg = metric.avg()
filepath = str(Path(filepath))
assert actual_count[filepath] == expected_count
assert actual_max[filepath] == expected_max
assert actual_avg[filepath] == expected_avg
def test_with_commits(path_to_repo, filepath, from_commit, to_commit, expected_count, expected_max, expected_avg):
metric = CodeChurn(path_to_repo=path_to_repo,
from_commit=from_commit,
to_commit=to_commit)
actual_count = metric.count()
actual_max = metric.max()
actual_avg = metric.avg()
filepath = str(Path(filepath))
assert actual_count[filepath] == expected_count
assert actual_max[filepath] == expected_max
assert actual_avg[filepath] == expected_avg
def fetch_metrics(self):
change_set = ChangeSet(self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
code_churn = CodeChurn(self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
commits_count = CommitsCount(self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
contributors_count = ContributorsCount(
self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
contributors_experience = ContributorsExperience(
self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
hunks_count = HunksCount(self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
line_count = LinesCount(self.url,
since=datetime.datetime.fromtimestamp(0),
to=datetime.datetime.now())
return {
'changeSet': change_set,
'codeChurn': code_churn,
'commitsCount': commits_count,
'contributorsCount': contributors_count,
'contributorsExperience': contributors_experience,
'hunksCount': hunks_count,
'lineCount': line_count,
}
def get_process_metrics(self, from_commit: str, to_commit: str) -> dict:
""" Extract process metrics for an evolution period.
Parameters
----------
from_commit : str
Hash of release start
to_commit : str
Hash of release end
"""
change_set = ChangeSet(self.path_to_repo, from_commit=from_commit, to_commit=to_commit)
code_churn = CodeChurn(self.path_to_repo, from_commit=from_commit, to_commit=to_commit, ignore_added_files=True)
commits_count = CommitsCount(self.path_to_repo, from_commit=from_commit, to_commit=to_commit)
contributors_count = ContributorsCount(self.path_to_repo, from_commit=from_commit, to_commit=to_commit)
highest_contributors_experience = ContributorsExperience(self.path_to_repo, from_commit=from_commit,
to_commit=to_commit)
median_hunks_count = HunksCount(self.path_to_repo, from_commit=from_commit, to_commit=to_commit)
lines_count = LinesCount(self.path_to_repo, from_commit=from_commit, to_commit=to_commit)
return {
'dict_change_set_max': change_set.max(),
'dict_change_set_avg': change_set.avg(),
'dict_code_churn_count': code_churn.count(),
'dict_code_churn_max': code_churn.max(),
'dict_code_churn_avg': code_churn.avg(),
'dict_commits_count': commits_count.count(),
'dict_contributors_count': contributors_count.count(),
'dict_minor_contributors_count': contributors_count.count_minor(),
'dict_highest_contributor_experience': highest_contributors_experience.count(),
'dict_hunks_median': median_hunks_count.count(),
'dict_additions': lines_count.count_added(),
'dict_additions_max': lines_count.max_added(),
'dict_additions_avg': lines_count.avg_added(),
'dict_deletions': lines_count.count_removed(),
'dict_deletions_max': lines_count.max_removed(),
'dict_deletions_avg': lines_count.avg_removed()}
for path, nloc in self.files.items():
sum_count[path] = sum(nloc)
return sum_count
import datetime as dt
dt1 = dt.datetime(2020, 1, 1, 0, 0)
dt2 = dt.datetime(2020, 5, 23, 0, 0)
measure = Complexity(
path_to_repo='https://github.com/eclipse/eclipse.jdt.core',
since=dt1,
to=dt2).count()
measure2 = CodeChurn(
path_to_repo='https://github.com/eclipse/eclipse.jdt.core',
since=dt1,
to=dt2).count()
measure3 = NLOC(path_to_repo='https://github.com/eclipse/eclipse.jdt.core',
since=dt1,
to=dt2).count()
measure4 = NLOC(path_to_repo='https://github.com/eclipse/eclipse.jdt.core',
since=dt1,
to=dt2).sumC()
fileData = open("/FileMetrics.txt", "w")
for file in measure:
values = str(measure[file]) + '/\\' + str(measure2[file]) + '/\\' +\
str(measure3[file]) + '/\\' + str(measure4[file])
fileData.write(str(file) + '/\\' + values + '\n')
fileData.close()
