def process_contributors_data(self, contributors: List[str]):
"""Pre process of data for contributors in a project repository."""
pr_ids = sorted([int(k) for k in self.pull_requests.keys()])
contributors_reviews_data: Dict[str, Any] = {}
contributors_reviews_data["reviewers"] = []
contributors_reviews_data["created_dts"] = []
interactions = {}
for contributor in contributors:
contributor_interaction = dict.fromkeys(contributors, 0)
interactions[contributor] = contributor_interaction
for pr_id in pr_ids:
pr = self.pull_requests[str(pr_id)]
self._analyze_pr_for_contributor_data(pr_id=pr_id, pr=pr, extracted_data=contributors_reviews_data)
self._analyze_contributors_interaction(
pr_interactions=pr["interactions"], pr_author=pr["created_by"], interactions_data=interactions
)
for reviewer in contributors_reviews_data["reviewers"]:
number_reviews = 0
reviews_length = []
time_reviews = []
for reviews in contributors_reviews_data[reviewer]["reviews"].values():
number_reviews += len(reviews)
review_words = 0
for review in reviews:
review_words += review["words_count"]
time_reviews.append(review["submitted_at"])
reviews_length.append(review_words)
last_review_dt = max(time_reviews)
contributors_reviews_data[reviewer]["number_reviews"] = number_reviews
contributors_reviews_data[reviewer]["median_review_length"] = np.median(reviews_length)
contributors_reviews_data[reviewer]["last_review_time"] = last_review_dt
# Encode Pull Request sizes for the contributor
contributor_prs_size_encoded = [
convert_score2num(label=pr_size) for pr_size in contributors_reviews_data[reviewer]["PRs_size"]
]
contributor_pr_median_size, contributor_relative_score = convert_num2label(
score=np.median(contributor_prs_size_encoded)
)
contributors_reviews_data[reviewer]["median_pr_length"] = contributor_pr_median_size
contributors_reviews_data[reviewer]["median_pr_length_score"] = contributor_relative_score
contributors_reviews_data[reviewer]["interactions"] = interactions[reviewer]
return contributors_reviews_data
def _analyze_pr_for_project_data(pr_id: int, pr: Dict[str, Any],
extracted_data: Dict[str, Any]):
"""Extract project data from Pull Request."""
if not pr["reviews"]:
return extracted_data
# Consider all approved reviews
pr_approved_dt = [
datetime.fromtimestamp(review["submitted_at"])
for review in pr["reviews"].values()
if review["state"] == "APPROVED"
]
if not pr_approved_dt:
return extracted_data
extracted_data["ids"].append(pr_id)
# PR created timestamp
pr_created_dt = datetime.fromtimestamp(pr["created_at"])
extracted_data["created_dts"].append(pr_created_dt)
# PR first review timestamp (no matter the contributor)
pr_first_review_dt = datetime.fromtimestamp(
[r for r in pr["reviews"].values()][0]["submitted_at"])
ttfr = (pr_first_review_dt - pr_created_dt).total_seconds() / 3600
extracted_data["TTFR"].append(ttfr)
mttfr = np.median(extracted_data["TTFR"])
extracted_data["MTTFR"].append(mttfr)
project_prs_size = pr["size"]
extracted_data["PRs_size"].append(project_prs_size)
extracted_data["encoded_PRs_size"].append(
convert_score2num(label=project_prs_size))
# Take maximum to consider last approved if more than one contributor has to approve
pr_approved_dt = max(pr_approved_dt)
ttr = (pr_approved_dt - pr_created_dt).total_seconds() / 3600
extracted_data["TTR"].append(ttr)
mttr = np.median(extracted_data["TTR"])
extracted_data["MTTR"].append(mttr)
# PR reviews timestamps
extracted_data["reviews_dts"] += [
r["submitted_at"] for r in pr["reviews"].values()
]
return extracted_data
def _evaluate_reviewer_data(pr: Dict[str, Any], reviewer: str,
review_submission_dt: datetime.timestamp,
extracted_data: Dict[str, Any]):
"""Evaluate reviewer data from reviews."""
if not pr["reviews"]:
return extracted_data
dt_approved = [
datetime.fromtimestamp(review["submitted_at"])
for review in pr["reviews"].values()
if review["state"] == "APPROVED" and review["author"] == reviewer
]
if not dt_approved:
return extracted_data
# PR created timestamp
pr_created_dt = datetime.fromtimestamp(pr["created_at"])
extracted_data["created_dts"].append(pr_created_dt)
pr_first_review_dt = datetime.fromtimestamp(review_submission_dt[0])
ttfr = (pr_first_review_dt - pr_created_dt).total_seconds() / 3600
extracted_data[reviewer]["TTFR"] = ttfr
mttfr = np.median(extracted_data[reviewer]["TTFR"])
extracted_data[reviewer]["MTTFR"].append(mttfr)
project_prs_size = pr["size"]
extracted_data[reviewer]["PRs_size"].append(project_prs_size)
extracted_data[reviewer]["encoded_PRs_size"].append(
convert_score2num(label=project_prs_size))
# Take maximum to consider last approved if more than one contributor has to approve
pr_approved_dt = max(dt_approved)
ttr = (pr_approved_dt - pr_created_dt).total_seconds() / 3600
extracted_data[reviewer]["TTR"].append(ttr)
mttr = np.median(extracted_data[reviewer]["TTR"])
extracted_data[reviewer]["MTTR"].append(mttr)
def visualize_pr_data(self, project: str, result_path: Path,
pr_data: Dict[str, Any]):
"""Visualize results from Pull Requests for a project."""
projects_reviews_data = self.processing.process_prs_project_data()
prs_ids = projects_reviews_data["ids"]
prs_created_dts = projects_reviews_data["created_dts"]
prs_lengths = projects_reviews_data["PRs_size"]
ttfr = projects_reviews_data["TTFR"]
mttfr = projects_reviews_data["MTTFR"]
# MTTFR
data = {"ids": prs_ids, "MTTFR": mttfr}
mttfr_per_pr_processed = self.analyze_outliers(quantity="MTTFR",
data=data)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in mttfr_per_pr_processed],
y_array=[el[1] for el in mttfr_per_pr_processed],
x_label="PR created date",
y_label="Median Time to First Review (h)",
title=f"MTTFR in Time per project: {project}",
output_name="MTTFR-in-time",
)
# TTFR
data = {
"ids": prs_ids,
"created_dts": prs_created_dts,
"TTFR": ttfr,
"lengths": prs_lengths
}
tfr_in_time_processed = self.analyze_outliers(quantity="TTFR",
data=data)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[1] for el in tfr_in_time_processed],
y_array=[el[2] for el in tfr_in_time_processed],
x_label="PR created date",
y_label="Time to First Review (h)",
title=f"TTFR in Time per project: {project}",
output_name="TTFR-in-time",
)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in tfr_in_time_processed],
y_array=[el[2] for el in tfr_in_time_processed],
x_label="PR id",
y_label="Time to First Review (h)",
title=f"TTFR per PR id per project: {project}",
output_name="TTFR-per-PR",
)
tfr_in_time_processed_sorted = sorted(
tfr_in_time_processed,
key=lambda x: convert_score2num(x[3]),
reverse=False)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[3] for el in tfr_in_time_processed_sorted],
y_array=[el[2] for el in tfr_in_time_processed_sorted],
x_label="PR length",
y_label="Time to First Review (h)",
title=f"TTFR in Time per PR length: {project}",
output_name="TTFR-per-PR-length",
)
ttr = projects_reviews_data["TTR"]
mttr = projects_reviews_data["MTTR"]
# MTTR
data = {"created_dts": prs_created_dts, "MTTR": mttr}
mttr_in_time_processed = self.analyze_outliers(quantity="MTTR",
data=data)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in mttr_in_time_processed],
y_array=[el[1] for el in mttr_in_time_processed],
x_label="PR created date",
y_label="Mean Time to Review (h)",
title=f"MTTR in Time per project: {project}",
output_name="MTTR-in-time",
)
# TTR
data = {
"ids": prs_ids,
"created_dts": prs_created_dts,
"TTR": ttr,
"lengths": prs_lengths
}
ttr_in_time_processed = self.analyze_outliers(quantity="TTR",
data=data)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[1] for el in ttr_in_time_processed],
y_array=[el[2] for el in ttr_in_time_processed],
x_label="PR created date",
y_label="Time to Review (h)",
title=f"TTR in Time per project: {project}",
output_name="TTR-in-time",
)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in ttr_in_time_processed],
y_array=[el[2] for el in ttr_in_time_processed],
x_label="PR id",
y_label="Time to Review (h)",
title=f"TTR per PR id per project: {project}",
output_name="TTR-per-PR",
)
ttr_in_time_processed_sorted = sorted(
ttr_in_time_processed,
key=lambda x: convert_score2num(x[3]),
reverse=False)
self.create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[3] for el in ttr_in_time_processed_sorted],
y_array=[el[2] for el in ttr_in_time_processed_sorted],
x_label="PR length",
y_label="Time to Review (h)",
title=f"TTR in Time per PR length: {project}",
output_name="TTR-per-PR-length",
)
def visualize_results(project: str):
"""Visualize results for a project."""
knowledge_path = Path.cwd().joinpath("./srcopsmetrics/bot_knowledge")
result_path = Path.cwd().joinpath("./srcopsmetrics/knowledge_statistics")
data = retrieve_knowledge(knowledge_path=knowledge_path, project=project)
if data:
projects_reviews_data = pre_process_project_data(data=data)
prs_ids = projects_reviews_data["ids"]
prs_created_dts = projects_reviews_data["created_dts"]
prs_lengths = projects_reviews_data["PRs_size"]
ttfr = projects_reviews_data["TTFR"]
mttfr = projects_reviews_data["MTTFR"]
# MTTFR
data = {
"ids": prs_ids,
"MTTFR": mttfr
}
mttfr_per_pr_processed = analyze_outliers(
quantity="MTTFR", data=data
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in mttfr_per_pr_processed],
y_array=[el[1] for el in mttfr_per_pr_processed],
x_label="PR created date",
y_label="Median Time to First Review (h)",
title=f"MTTFR in Time per project: {project}",
output_name="MTTFR-in-time",
)
# TTFR
data = {
"ids": prs_ids,
"created_dts": prs_created_dts,
"TTFR": ttfr,
"lengths": prs_lengths
}
tfr_in_time_processed = analyze_outliers(
quantity="TTFR", data=data
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[1] for el in tfr_in_time_processed],
y_array=[el[2] for el in tfr_in_time_processed],
x_label="PR created date",
y_label="Time to First Review (h)",
title=f"TTFR in Time per project: {project}",
output_name="TTFR-in-time",
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in tfr_in_time_processed],
y_array=[el[2] for el in tfr_in_time_processed],
x_label="PR id",
y_label="Time to First Review (h)",
title=f"TTFR per PR id per project: {project}",
output_name="TTFR-per-PR",
)
tfr_in_time_processed_sorted = sorted(
tfr_in_time_processed, key=lambda x: convert_score2num(x[3]), reverse=False
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[3] for el in tfr_in_time_processed_sorted],
y_array=[el[2] for el in tfr_in_time_processed_sorted],
x_label="PR length",
y_label="Time to First Review (h)",
title=f"TTFR in Time per PR length: {project}",
output_name="TTFR-per-PR-length",
)
ttr = projects_reviews_data["TTR"]
mttr = projects_reviews_data["MTTR"]
# MTTR
data = {
"created_dts": prs_created_dts,
"MTTR": mttr,
}
mttr_in_time_processed = analyze_outliers(
quantity="MTTR", data=data
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in mttr_in_time_processed],
y_array=[el[1] for el in mttr_in_time_processed],
x_label="PR created date",
y_label="Mean Time to Review (h)",
title=f"MTTR in Time per project: {project}",
output_name="MTTR-in-time",
)
# TTR
data = {
"ids": prs_ids,
"created_dts": prs_created_dts,
"TTR": ttr,
"lengths": prs_lengths
}
ttr_in_time_processed = analyze_outliers(
quantity="TTR", data=data
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[1] for el in ttr_in_time_processed],
y_array=[el[2] for el in ttr_in_time_processed],
x_label="PR created date",
y_label="Time to Review (h)",
title=f"TTR in Time per project: {project}",
output_name="TTR-in-time",
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[0] for el in ttr_in_time_processed],
y_array=[el[2] for el in ttr_in_time_processed],
x_label="PR id",
y_label="Time to Review (h)",
title=f"TTR per PR id per project: {project}",
output_name="TTR-per-PR",
)
ttr_in_time_processed_sorted = sorted(
ttr_in_time_processed, key=lambda x: convert_score2num(x[3]), reverse=False
)
create_per_pr_plot(
result_path=result_path,
project=project,
x_array=[el[3] for el in ttr_in_time_processed_sorted],
y_array=[el[2] for el in ttr_in_time_processed_sorted],
x_label="PR length",
y_label="Time to Review (h)",
title=f"TTR in Time per PR length: {project}",
output_name="TTR-per-PR-length",
)