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importos
fromsettingsimportROOTDIR, CM, PR, IS, BOT
importpandasaspd
importnumpyasnp
fromtypingimportTypeVar
frommarkdownimportmarkdown
frombs4importBeautifulSoup
importre
Markdown=TypeVar('Markdown')
repo_path=os.path.join(ROOTDIR, "valid_repos.csv")
repos=pd.read_csv(repo_path)
defrn_stats():
defvalidate_rn(rn: Markdown, valid_link_num: int=None) ->intorbool:
# if not rn:
# return False
# html = markdown(rn)
# soup = BeautifulSoup(html, "html.parser")
# all_a = soup.find_all('a')
# cnt = 0
# for a in all_a:
# try:
# link = a["href"]
# if re.match(CM, link) or re.match(PR, link) or re.match(IS, link):
# cnt += 1
# except KeyError:
# continue
# if not valid_link_num:
# return cnt
# else:
# return cnt >= valid_link_num
pass
num_rn= []
# release_has_link = []
oldest=latest= []
num_valid_rn=0
num_empty_rn=0
# bot_like_author = []
# num_bot_author_rn = 0
# num_miss_author_info = 0
# suspicious_repo = []
average_link_in_repo= []
forrepoinrepos["Repo"]:
print(repo)
folder=os.path.join(ROOTDIR, "data", repo.replace('/', '_'))
rn=pd.read_csv(os.path.join(folder, "rn_info_sorted.csv"))
rn["published_at"] =pd.to_datetime(rn["published_at"])
num_rn.append(len(rn))
oldest.append(rn["published_at"].iloc[-1])
latest.append(rn["published_at"].iloc[0])
empty_rn=rn["body"].isnull()
num_empty_rn+=sum(empty_rn)
# miss_author_info = rn["author"].isnull()
# num_miss_author_info += sum(miss_author_info)
# check_bot = False
# total_link = 0
foriinrange(len(rn)):
# if not miss_author_info[i]:
# author_info = eval(rn.loc[i, "author"])
# login = author_info["login"]
# if BOT.findall(login):
# bot_like_author.append(login)
# num_bot_author_rn += 1
# check_bot = True
ifnotempty_rn[i]:
release_note=rn.loc[i, "body"]
ifvalidate_rn(release_note, 3):
# release_has_link.append({"Repo": repo, "Tag": rn.loc[i, "tag_name"], "Time": rn.loc[i, "published_at"]})
num_valid_rn+=1
# if not empty_rn[i]:
# release_note = rn.loc[i, "body"]
# total_link += validate_rn(release_note)
release_has_link=pd.DataFrame(release_has_link, columns=["Repo", "Tag", "Time"])
release_has_link=release_has_link.sort_values(by="Time", ignore_index=True)
release_has_link.to_csv("release_has_link.csv")
# average_link_in_repo.append(total_link / len(rn))
# if check_bot:
# suspicious_repo.append(repo)
# oldest = [x.to_pydatetime() for x in oldest]
# latest = [x.to_pydatetime() for x in latest]
# num_rn = np.array(num_rn)
# print("Total num release note:", np.sum(num_rn))
# print("Mean num release note:", np.mean(num_rn))
# print("Max num release note:", np.max(num_rn))
# print("Min num release note:", np.min(num_rn))
# print("Median num release note:", np.median(num_rn))
# print("Oldest release note:", min(oldest))
# print("Latest release note:", max(latest))
# print("Num empty release note:", num_empty_rn)
# print("Num release note has at least 3 link to change descriptor", num_valid_rn)
# print("Num release note missing author info:", num_miss_author_info)
# print("Num bot like release note author:", num_bot_author_rn)
# bot_like_author = pd.DataFrame({"bot_like_author": bot_like_author})
# bot_like_author.to_csv("bot_like_author_release_note.csv")
# suspicious_repo = pd.DataFrame({"Suspicious repo": suspicious_repo})
# suspicious_repo.to_csv("suspicious_repo.csv")
# average_link_in_repo = pd.DataFrame({"Repo": repos["Repo"], "Average link": average_link_in_repo})
# average_link_in_repo.to_csv("average_link_in_repo.csv")
defcm_stats():
num_cm=0
num_empty_commit_message=0
num_bot_author_commit=0
bot_like_author= []
num_miss_author_info=0
suspicious_commit= []
forrepoinrepos["Repo"]:
print(repo)
folder=os.path.join(ROOTDIR, "data", repo.replace('/', '_'))
commit_info=pd.read_csv(os.path.join(folder, "commit_sorted.csv"))
num_cm+=len(commit_info)
empty_message=commit_info["Summary"].isnull()
num_empty_commit_message+=sum(empty_message)
miss_author_info=commit_info["Author"].isnull()
num_miss_author_info+=sum(miss_author_info)
check_bot=False
foriinrange(len(commit_info)):
ifnotmiss_author_info[i]:
author=commit_info.loc[i, "Author"]
ifBOT.findall(author):
bot_like_author.append(author)
num_bot_author_commit+=1
check_bot=True
ifcheck_bot:
suspicious_commit.append(repo)
print("Total commit:", num_cm)
print("Num empty commit message:", num_empty_commit_message)
print("Num miss author info commit:", num_miss_author_info)
print("Num bot like author commit:", num_bot_author_commit)
bot_like_author=pd.DataFrame({"bot_like_author": bot_like_author})
bot_like_author.to_csv("bot_like_author_commit.csv")
suspicious_commit=pd.DataFrame({"Suspicious commit repo": suspicious_commit})
suspicious_commit.to_csv("suspicious_commit_repo.csv")
defissue_stats():
total_issue=0
bot_user_num=0
suspicious_issue_repo= []
bot_like_user=set()
forrepoinrepos["Repo"]:
print(repo)
folder=os.path.join(ROOTDIR, "data", repo.replace('/', '_'))
issue_info=pd.read_csv(os.path.join(folder, "issue_info_sorted.csv"), dtype="object",
engine="python", on_bad_lines="skip")
miss_user_info=issue_info["user"].isnull()
total_issue+=len(issue_info)
check_bot=False
foriinrange(len(issue_info)):
ifnotmiss_user_info[i]:
user=issue_info.loc[i, "user"]
ifBOT.findall(user):
bot_like_user.add(user)
bot_user_num+=1
check_bot=True
ifcheck_bot:
suspicious_issue_repo.append(repo)
print("Total issue:", total_issue)
print("Num issue has bot like user", bot_user_num)
bot_like_user=pd.DataFrame({"bot_like_user": list(bot_like_user)})
bot_like_user.to_csv("bot_like_user_issue.csv")
suspicious_issue_repo=pd.DataFrame({"Suspicious issue repo": suspicious_issue_repo})
suspicious_issue_repo.to_csv("suspicious_issue_repo.csv")
defpr_stats():
total_pr=0
bot_user_num=0
suspicious_pr_repo= []
bot_like_user=set()
forrepoinrepos["Repo"]:
print(repo)
folder=os.path.join(ROOTDIR, "data", repo.replace('/', '_'))
pr_info=pd.read_csv(os.path.join(folder, "pr_info_sorted.csv"), dtype="object",
engine="python", on_bad_lines="skip")
miss_user_info=pr_info["user"].isnull()
total_pr+=len(pr_info)
check_bot=False
foriinrange(len(pr_info)):
ifnotmiss_user_info[i]:
user=pr_info.loc[i, "user"]
ifBOT.findall(user):
bot_like_user.add(user)
bot_user_num+=1
check_bot=True
ifcheck_bot:
suspicious_pr_repo.append(repo)
print("Total pr:", total_pr)
print("Num pr has bot like user", bot_user_num)
bot_like_user=pd.DataFrame({"bot_like_user": list(bot_like_user)})
bot_like_user.to_csv("bot_like_user_pr.csv")
suspicious_pr_repo=pd.DataFrame({"Suspicious pr repo": suspicious_pr_repo})
suspicious_pr_repo.to_csv("suspicious_pr_repo.csv")
defstatistic():
release_has_link=pd.read_csv("release_has_link.csv")
release_has_link["Time"] =pd.to_datetime(release_has_link["Time"])
release_has_link["Year"] =release_has_link["Time"].dt.year
print(release_has_link.groupby(by=["Year"])["Time"].count())
# print(release_has_link.info())