def main():
unstable = subprocess.Popen('grep "\[Fedora Update\] \[unstable\]" bodhi.logs',
stdout=subprocess.PIPE, shell=True)
out, err = unstable.communicate()
(unstable_updates, unstable_critpath, unstable_deltas,
unstable_accum, unstable_occur) = parse_output(out)
stable = subprocess.Popen('grep "\[Fedora Update\] \[stablekarma\]" bodhi.logs',
stdout=subprocess.PIPE, shell=True)
out, err = stable.communicate()
(stable_updates, stable_critpath, stable_deltas,
stable_accum, stable_occur) = parse_output(out)
for release in Release.select():
print '\n' + header(release.long_name)
num_updates = PackageUpdate.select(
PackageUpdate.q.releaseID==release.id).count()
num_stable = len(stable_updates[release.name])
num_unstable = len(unstable_updates[release.name])
num_testing = len(unstable_deltas) + len(stable_deltas)
print " * %d updates automatically unpushed due to karma (%0.2f%%)" % (
num_unstable, float(num_unstable) / num_updates * 100)
print " * %d of which were critical path updates" % (
unstable_critpath[release.name])
print " * %d updates automatically pushed due to karma (%0.2f%%)" % (
num_stable, float(num_stable) / num_updates * 100)
print " * %d of which were critical path updates" % (
stable_critpath[release.name])
print " * Time spent in testing of updates that were pushed by karma:"
print " * mean = %d days" % (stable_accum.days / len(stable_deltas))
print " * median = %d days" % stable_deltas[len(stable_deltas)/2].days
print " * mode = %d days" % sorted(stable_occur.items(),
key=itemgetter(1))[-1][0]
print " * Time spent in testing of updates that were unpushed by karma:"
print " * mean = %d days" % (unstable_accum.days / len(unstable_deltas))
print " * median = %d days" % unstable_deltas[len(unstable_deltas)/2].days
print " * mode = %d days" % sorted(unstable_occur.items(),
key=itemgetter(1))[-1][0]
def main():
unstable = subprocess.Popen('grep "\[Fedora Update\] \[unstable\]" bodhi.logs',
stdout=subprocess.PIPE, shell=True)
out, err = unstable.communicate()
(unstable_updates, unstable_critpath, unstable_deltas,
unstable_accum, unstable_occur) = parse_output(out)
stable = subprocess.Popen('grep "\[Fedora Update\] \[stablekarma\]" bodhi.logs',
stdout=subprocess.PIPE, shell=True)
out, err = stable.communicate()
(stable_updates, stable_critpath, stable_deltas,
stable_accum, stable_occur) = parse_output(out)
for release in Release.select():
print '\n' + header(release.long_name)
num_updates = PackageUpdate.select(
PackageUpdate.q.releaseID==release.id).count()
num_stable = len(stable_updates[release.name])
num_unstable = len(unstable_updates[release.name])
num_testing = len(unstable_deltas) + len(stable_deltas)
print " * %d updates automatically unpushed due to karma (%0.2f%%)" % (
num_unstable, float(num_unstable) / num_updates * 100)
print " * %d of which were critical path updates" % (
unstable_critpath[release.name])
print " * %d updates automatically pushed due to karma (%0.2f%%)" % (
num_stable, float(num_stable) / num_updates * 100)
print " * %d of which were critical path updates" % (
stable_critpath[release.name])
print " * Time spent in testing of updates that were pushed by karma:"
print " * mean = %d days" % (stable_accum.days / len(stable_deltas))
print " * median = %d days" % stable_deltas[len(stable_deltas)/2].days
print " * mode = %d days" % sorted(stable_occur.items(),
key=itemgetter(1))[-1][0]
print " * Time spent in testing of updates that were unpushed by karma:"
print " * mean = %d days" % (unstable_accum.days / len(unstable_deltas))
print " * median = %d days" % unstable_deltas[len(unstable_deltas)/2].days
print " * mode = %d days" % sorted(unstable_occur.items(),
key=itemgetter(1))[-1][0]
def main():
load_config()
stats = {} # {release: {'stat': ...}}
feedback = 0 # total number of updates that received feedback
karma = defaultdict(int) # {username: # of karma submissions}
num_updates = PackageUpdate.select().count()
proventesters = set()
for release in Release.select():
print header(release.long_name)
updates = PackageUpdate.select(PackageUpdate.q.releaseID==release.id)
stats[release.name] = {
'num_updates': updates.count(),
'num_tested': 0,
'num_tested_without_karma': 0,
'num_feedback': 0,
'num_anon_feedback': 0,
'num_critpath': 0,
'num_critpath_approved': 0,
'num_critpath_unapproved': 0,
'num_stablekarma': 0,
'num_testingtime': 0,
'critpath_without_karma': set(),
'conflicted_proventesters': [],
'critpath_positive_karma_including_proventesters': [],
'critpath_positive_karma_negative_proventesters': [],
'stable_with_negative_karma': PackageUpdate.select(
AND(PackageUpdate.q.releaseID==release.id,
PackageUpdate.q.status=='stable',
PackageUpdate.q.karma < 0)).count(),
'bugs': set(),
'karma': defaultdict(int),
'deltas': [],
'occurrences': {},
'accumulative': timedelta(),
'packages': defaultdict(int),
'proventesters': set(),
'proventesters_1': 0,
'proventesters_0': 0,
'proventesters_-1': 0,
# for tracking number of types of karma
'1': 0,
'0': 0,
'-1': 0,
}
data = stats[release.name]
for status in statuses:
data['num_%s' % status] = PackageUpdate.select(AND(
PackageUpdate.q.releaseID==release.id,
PackageUpdate.q.status==status)).count()
for type in types:
data['num_%s' % type] = PackageUpdate.select(AND(
PackageUpdate.q.releaseID==release.id,
PackageUpdate.q.type==type)).count()
for update in release.updates:
for build in update.builds:
data['packages'][build.package] += 1
for bug in update.bugs:
data['bugs'].add(bug.bz_id)
feedback_done = False
testingtime_done = False
for comment in update.comments:
if comment.author_name in ('autoqa', 'taskotron'):
continue
# Track the # of +1's, -1's, and +0's.
if comment.author_name != 'bodhi':
data[str(comment.karma)] += 1
if comment.author_group == 'proventesters':
data['proventesters'].add(comment.author_name)
data['proventesters_%d' % comment.karma] += 1
if comment.text == 'This update has reached the stable karma threshold and will be pushed to the stable updates repository':
data['num_stablekarma'] += 1
elif comment.text and comment.text.endswith('days in testing and can be pushed to stable now if the maintainer wishes'):
data['num_testingtime'] += 1
# For figuring out if an update has received feedback or not
if not feedback_done:
if (not comment.author.startswith('bodhi') and
comment.karma != 0 and not comment.anonymous):
data['num_feedback'] += 1 # per-release tracking of feedback
feedback += 1 # total number of updates that have received feedback
feedback_done = True # so we don't run this for each comment
# Tracking per-author karma & anonymous feedback
if not comment.author.startswith('bodhi'):
if comment.anonymous:
# @@: should we track anon +0 comments as "feedback"?
if comment.karma != 0:
data['num_anon_feedback'] += 1
else:
author = comment.author_name
data['karma'][author] += 1
karma[author] += 1
if (not testingtime_done and
comment.text == 'This update has been pushed to testing'):
for othercomment in update.comments:
if othercomment.text == 'This update has been pushed to stable':
delta = othercomment.timestamp - comment.timestamp
data['deltas'].append(delta)
data['occurrences'][delta.days] = \
data['occurrences'].setdefault(
delta.days, 0) + 1
data['accumulative'] += delta
testingtime_done = True
break
if update.critpath:
if update.critpath_approved or update.status == 'stable':
data['num_critpath_approved'] += 1
else:
if status in ('testing', 'pending'):
data['num_critpath_unapproved'] += 1
data['num_critpath'] += 1
#if not feedback_done:
if update.status == 'stable' and update.karma == 0:
data['critpath_without_karma'].add(update)
# Proventester metrics
proventester_karma = defaultdict(int) # {username: karma}
positive_proventesters = 0
negative_proventesters = 0
for comment in update.comments:
if comment.author_group == 'proventesters':
proventester_karma[comment.author_name] += comment.karma
for _karma in proventester_karma.values():
if _karma > 0:
positive_proventesters += 1
elif _karma < 0:
negative_proventesters += 1
# Conflicting proventesters
if positive_proventesters and negative_proventesters:
data['conflicted_proventesters'] += [short_url(update)]
# Track updates with overall positive karma, including positive
# karma from a proventester
if update.karma > 0 and positive_proventesters:
data['critpath_positive_karma_including_proventesters'] += [short_url(update)]
# Track updates with overall positive karma, including negative
# karma from a proventester
if update.karma > 0 and negative_proventesters:
data['critpath_positive_karma_negative_proventesters'] += [short_url(update)]
if testingtime_done:
data['num_tested'] += 1
if not feedback_done:
data['num_tested_without_karma'] += 1
data['deltas'].sort()
print " * %d updates" % data['num_updates']
print " * %d packages updated" % (len(data['packages']))
for status in statuses:
print " * %d %s updates" % (data['num_%s' % status], status)
for type in types:
print " * %d %s updates (%0.2f%%)" % (data['num_%s' % type], type,
float(data['num_%s' % type]) / data['num_updates'] * 100)
print " * %d bugs resolved" % len(data['bugs'])
print " * %d critical path updates (%0.2f%%)" % (data['num_critpath'],
float(data['num_critpath']) / data['num_updates'] * 100)
print " * %d approved critical path updates" % (
data['num_critpath_approved'])
print " * %d unapproved critical path updates" % (
data['num_critpath_unapproved'])
print " * %d updates received feedback (%0.2f%%)" % (
data['num_feedback'], (float(data['num_feedback']) /
data['num_updates'] * 100))
print " * %d +0 comments" % data['0']
print " * %d +1 comments" % data['1']
print " * %d -1 comments" % data['-1']
print " * %d unique authenticated karma submitters" % (
len(data['karma']))
print " * %d proventesters" % len(data['proventesters'])
print " * %d +1's from proventesters" % data['proventesters_1']
print " * %d -1's from proventesters" % data['proventesters_-1']
if data['num_critpath']:
print " * %d critpath updates with conflicting proventesters (%0.2f%% of critpath)" % (len(data['conflicted_proventesters']), float(len(data['conflicted_proventesters'])) / data['num_critpath'] * 100)
for u in data['conflicted_proventesters']:
print " * " + u
print " * %d critpath updates with positive karma and negative proventester feedback (%0.2f%% of critpath)" % (len(data['critpath_positive_karma_negative_proventesters']), float(len(data['critpath_positive_karma_negative_proventesters'])) / data['num_critpath'] * 100)
for u in data['critpath_positive_karma_negative_proventesters']:
print " * " + u
print " * %d critpath updates with positive karma and positive proventester feedback (%0.2f%% of critpath)" % (len(data['critpath_positive_karma_including_proventesters']), float(len(data['critpath_positive_karma_including_proventesters'])) / data['num_critpath'] * 100)
print " * %d anonymous users gave feedback (%0.2f%%)" % (
data['num_anon_feedback'], float(data['num_anon_feedback']) /
(data['num_anon_feedback'] + sum(data['karma'].values())) * 100)
# This does not take into account updates that reach stablekarma before being pushed to testing!
# print " * %d out of %d stable updates went through testing (%0.2f%%)" %(
# data['num_tested'], data['num_stable'],
# float(data['num_tested']) / data['num_stable'] * 100)
print " * %d updates reached the stable karma threshold (%0.2f%%)" % (
data['num_stablekarma'],
float(data['num_stablekarma']) / data['num_stable'] * 100)
print " * %d updates reached the minimum time in testing threshold (%0.2f%%)" % (
data['num_testingtime'],
float(data['num_testingtime']) / data['num_stable'] * 100)
print " * %d went from testing to stable *without* karma (%0.2f%%)" % (
data['num_tested_without_karma'],
float(data['num_tested_without_karma']) /
data['num_tested'] * 100)
print " * %d updates were pushed to stable with negative karma (%0.2f%%)" % (
data['stable_with_negative_karma'], float(data['stable_with_negative_karma']) / data['num_stable'] * 100)
print " * %d critical path updates pushed to stable *without* karma" % (
len(data['critpath_without_karma']))
#for update in data['critpath_without_karma']:
# print " * %s submitted by %s" % (update.title, update.submitter)
print " * Time spent in testing:"
print " * mean = %d days" % (data['accumulative'].days /
len(data['deltas']))
print " * median = %d days" % (
data['deltas'][len(data['deltas']) / 2].days)
print " * mode = %d days" % (
sorted(data['occurrences'].items(), key=itemgetter(1))[-1][0])
#for package in sorted(data['packages'].items(), key=itemgetter(1), reverse=True):
# print " * %s: %d" % (package[0].name, package[1])
print
print
print "Out of %d total updates, %d received feedback (%0.2f%%)" % (
num_updates, feedback, (float(feedback) / num_updates * 100))
print "Out of %d total unique commenters, the top 50 are:" % (
len(karma))
for submitter in sorted(karma.iteritems(), key=itemgetter(1), reverse=True)[:50]:
print " * %s (%d)" % (submitter[0], submitter[1])
def main(releases=None):
db = get_db_from_config()
stats = {} # {release: {'stat': ...}}
feedback = 0 # total number of updates that received feedback
karma = defaultdict(int) # {username: # of karma submissions}
num_updates = db.query(Update).count()
proventesters = set()
for release in db.query(Release).all():
if releases and release.name not in releases:
continue
updates = db.query(Update).filter_by(release=release)
critpath_pkgs = get_critpath_pkgs(release.name.lower())
total = updates.count()
if not total:
continue
print header(release.long_name)
stats[release.name] = {
'num_updates': total,
'num_tested': 0,
'num_tested_without_karma': 0,
'num_feedback': 0,
'num_anon_feedback': 0,
'critpath_pkgs': defaultdict(int),
'num_critpath': 0,
'num_critpath_approved': 0,
'num_critpath_unapproved': 0,
'num_stablekarma': 0,
'num_testingtime': 0,
'critpath_without_karma': set(),
'conflicted_proventesters': [],
'critpath_positive_karma_including_proventesters': [],
'critpath_positive_karma_negative_proventesters': [],
'stable_with_negative_karma': updates.filter(
and_(Update.status == UpdateStatus.stable,
Update.karma < 0)).count(),
'bugs': set(),
'karma': defaultdict(int),
'deltas': [],
'occurrences': {},
'accumulative': timedelta(),
'packages': defaultdict(int),
'proventesters': set(),
'proventesters_1': 0,
'proventesters_0': 0,
'proventesters_-1': 0,
'submitters': defaultdict(int),
# for tracking number of types of karma
'1': 0,
'0': 0,
'-1': 0,
}
data = stats[release.name]
for status in statuses:
data['num_%s' % status] = updates.filter(
and_(Update.status == UpdateStatus.from_string(status))).count()
for type in types:
data['num_%s' % type] = updates.filter(
Update.type == UpdateType.from_string(type)).count()
for update in updates.all():
assert update.user, update.title
data['submitters'][update.user.name] += 1
for build in update.builds:
data['packages'][build.package.name] += 1
if build.package.name in critpath_pkgs:
data['critpath_pkgs'][build.package.name] += 1
for bug in update.bugs:
data['bugs'].add(bug.bug_id)
feedback_done = False
testingtime_done = False
stablekarma_done = False
for comment in update.comments:
if not comment.user:
print('Error: None comment for %s' % update.title)
if comment.user.name == 'autoqa':
continue
# Track the # of +1's, -1's, and +0's.
if comment.user.name != 'bodhi':
data[str(comment.karma)] += 1
if 'proventesters' in [g.name for g in comment.user.groups]:
data['proventesters'].add(comment.user.name)
data['proventesters_%d' % comment.karma] += 1
if update.status == UpdateStatus.stable:
if not stablekarma_done:
if comment.text == 'This update has reached the stable karma threshold and will be pushed to the stable updates repository':
data['num_stablekarma'] += 1
stablekarma_done = True
elif comment.text and comment.text.endswith('days in testing and can be pushed to stable now if the maintainer wishes'):
data['num_testingtime'] += 1
stablekarma_done = True
# For figuring out if an update has received feedback or not
if not feedback_done:
if (not comment.user.name == 'bodhi'
and comment.karma != 0 and not comment.anonymous):
data['num_feedback'] += 1 # per-release tracking of feedback
feedback += 1 # total number of updates that have received feedback
feedback_done = True # so we don't run this for each comment
# Tracking per-author karma & anonymous feedback
if not comment.user.name == 'bodhi':
if comment.anonymous:
# @@: should we track anon +0 comments as "feedback"?
if comment.karma != 0:
data['num_anon_feedback'] += 1
else:
author = comment.user.name
data['karma'][author] += 1
karma[author] += 1
if (not testingtime_done and
comment.text == 'This update has been pushed to testing'):
for othercomment in update.comments:
if othercomment.text == 'This update has been pushed to stable':
delta = othercomment.timestamp - comment.timestamp
data['deltas'].append(delta)
data['occurrences'][delta.days] = \
data['occurrences'].setdefault(
delta.days, 0) + 1
data['accumulative'] += delta
testingtime_done = True
break
if update.critpath:
if update.critpath_approved or update.status == UpdateStatus.stable:
data['num_critpath_approved'] += 1
else:
if update.status in (UpdateStatus.testing, UpdateStatus.pending):
data['num_critpath_unapproved'] += 1
data['num_critpath'] += 1
#if not feedback_done:
if update.status == UpdateStatus.stable and update.karma == 0:
data['critpath_without_karma'].add(update)
# Proventester metrics
proventester_karma = defaultdict(int) # {username: karma}
positive_proventesters = 0
negative_proventesters = 0
for comment in update.comments:
if 'proventesters' in [g.name for g in comment.user.groups]:
proventester_karma[comment.user.name] += comment.karma
for _karma in proventester_karma.values():
if _karma > 0:
positive_proventesters += 1
elif _karma < 0:
negative_proventesters += 1
# Conflicting proventesters
if positive_proventesters and negative_proventesters:
data['conflicted_proventesters'] += [short_url(update)]
# Track updates with overall positive karma, including positive
# karma from a proventester
if update.karma > 0 and positive_proventesters:
data['critpath_positive_karma_including_proventesters'] += [short_url(update)]
# Track updates with overall positive karma, including negative
# karma from a proventester
if update.karma > 0 and negative_proventesters:
data['critpath_positive_karma_negative_proventesters'] += [short_url(update)]
if testingtime_done:
data['num_tested'] += 1
if not feedback_done:
data['num_tested_without_karma'] += 1
data['deltas'].sort()
print " * %d updates" % data['num_updates']
print " * %d packages updated" % (len(data['packages']))
for status in statuses:
print " * %d %s updates" % (data['num_%s' % status], status)
for type in types:
print " * %d %s updates (%0.2f%%)" % (data['num_%s' % type], type,
float(data['num_%s' % type]) / data['num_updates'] * 100)
print " * %d bugs resolved" % len(data['bugs'])
print " * %d critical path updates (%0.2f%%)" % (data['num_critpath'],
float(data['num_critpath']) / data['num_updates'] * 100)
print " * %d approved critical path updates" % (
data['num_critpath_approved'])
print " * %d unapproved critical path updates" % (
data['num_critpath_unapproved'])
print " * %d updates received feedback (%0.2f%%)" % (
data['num_feedback'], (float(data['num_feedback']) /
data['num_updates'] * 100))
print " * %d +0 comments" % data['0']
print " * %d +1 comments" % data['1']
print " * %d -1 comments" % data['-1']
print " * %d unique authenticated karma submitters" % (
len(data['karma']))
print " * %d proventesters" % len(data['proventesters'])
print " * %d +1's from proventesters" % data['proventesters_1']
print " * %d -1's from proventesters" % data['proventesters_-1']
if data['num_critpath']:
print " * %d critpath updates with conflicting proventesters (%0.2f%% of critpath)" % (len(data['conflicted_proventesters']), float(len(data['conflicted_proventesters'])) / data['num_critpath'] * 100)
for u in sorted(data['conflicted_proventesters']):
print ' <li><a href="%s">%s</a></li>' % (u, u.split('/')[-1])
print " * %d critpath updates with positive karma and negative proventester feedback (%0.2f%% of critpath)" % (len(data['critpath_positive_karma_negative_proventesters']), float(len(data['critpath_positive_karma_negative_proventesters'])) / data['num_critpath'] * 100)
for u in sorted(data['critpath_positive_karma_negative_proventesters']):
print ' <li><a href="%s">%s</a></li>' % (u, u.split('/')[-1])
print " * %d critpath updates with positive karma and positive proventester feedback (%0.2f%% of critpath)" % (len(data['critpath_positive_karma_including_proventesters']), float(len(data['critpath_positive_karma_including_proventesters'])) / data['num_critpath'] * 100)
print " * %d anonymous users gave feedback (%0.2f%%)" % (
data['num_anon_feedback'], float(data['num_anon_feedback']) /
(data['num_anon_feedback'] + sum(data['karma'].values())) * 100)
# This does not take into account updates that reach stablekarma before being pushed to testing!
# print " * %d out of %d stable updates went through testing (%0.2f%%)" %(
# data['num_tested'], data['num_stable'],
# float(data['num_tested']) / data['num_stable'] * 100)
print " * %d stable updates reached the stable karma threshold (%0.2f%%)" %(
data['num_stablekarma'],
float(data['num_stablekarma']) / data['num_stable'] * 100)
print " * %d stable updates reached the minimum time in testing threshold (%0.2f%%)" % (
data['num_testingtime'],
float(data['num_testingtime']) / data['num_stable'] * 100)
print " * %d went from testing to stable *without* karma (%0.2f%%)" %(
data['num_tested_without_karma'],
float(data['num_tested_without_karma']) /
data['num_tested'] * 100)
print " * %d updates were pushed to stable with negative karma (%0.2f%%)" % (
data['stable_with_negative_karma'], float(data['stable_with_negative_karma']) / data['num_stable'] * 100)
print " * %d critical path updates pushed to stable *without* karma" % (
len(data['critpath_without_karma']))
#for update in data['critpath_without_karma']:
# print " * %s submitted by %s" % (update.title, update.submitter)
print " * Time spent in testing:"
print " * mean = %d days" % (data['accumulative'].days /
len(data['deltas']))
print " * median = %d days" % (
data['deltas'][len(data['deltas']) / 2].days)
print " * mode = %d days" % (
sorted(data['occurrences'].items(), key=itemgetter(1))[-1][0])
print "Out of %d packages updated, the top 50 were:" % (
len(data['packages']))
for package in sorted(data['packages'].iteritems(), key=itemgetter(1), reverse=True)[:50]:
print " * %s (%d)" % (package[0], package[1])
print "Out of %d update submitters, the top 50 were:" % (
len(data['submitters']))
for submitter in sorted(data['submitters'].iteritems(), key=itemgetter(1), reverse=True)[:50]:
print " * %s (%d)" % (submitter[0], submitter[1])
print "Out of %d critical path updates, the top 50 updated were:" % (
len(data['critpath_pkgs']))
for x in sorted(data['critpath_pkgs'].iteritems(), key=itemgetter(1), reverse=True)[:50]:
print " * %s (%d)" % (x[0], x[1])
critpath_not_updated = set()
for pkg in critpath_pkgs:
if pkg not in data['critpath_pkgs']:
critpath_not_updated.add(pkg)
print "Out of %d critical path packages, %d were never updated:" % (
len(critpath_pkgs), len(critpath_not_updated))
for pkg in sorted(critpath_not_updated):
print(' * %s' % pkg)
print
print
print "Out of %d total updates, %d received feedback (%0.2f%%)" % (
num_updates, feedback, (float(feedback) / num_updates * 100))
print "Out of %d total unique commenters, the top 50 were:" % (
len(karma))
for submitter in sorted(karma.iteritems(), key=itemgetter(1), reverse=True)[:50]:
print " * %s (%d)" % (submitter[0], submitter[1])
def main():
load_config()
stats = {} # {release: {'stat': ...}}
feedback = 0 # total number of updates that received feedback
karma = defaultdict(int) # {username: # of karma submissions}
num_updates = PackageUpdate.select().count()
proventesters = set()
for release in Release.select():
print header(release.long_name)
updates = PackageUpdate.select(PackageUpdate.q.releaseID == release.id)
stats[release.name] = {
'num_updates':
updates.count(),
'num_tested':
0,
'num_tested_without_karma':
0,
'num_feedback':
0,
'num_anon_feedback':
0,
'num_critpath':
0,
'num_critpath_approved':
0,
'num_critpath_unapproved':
0,
'num_stablekarma':
0,
'num_testingtime':
0,
'critpath_without_karma':
set(),
'conflicted_proventesters': [],
'critpath_positive_karma_including_proventesters': [],
'critpath_positive_karma_negative_proventesters': [],
'stable_with_negative_karma':
PackageUpdate.select(
AND(PackageUpdate.q.releaseID == release.id,
PackageUpdate.q.status == 'stable',
PackageUpdate.q.karma < 0)).count(),
'bugs':
set(),
'karma':
defaultdict(int),
'deltas': [],
'occurrences': {},
'accumulative':
timedelta(),
'packages':
defaultdict(int),
'proventesters':
set(),
'proventesters_1':
0,
'proventesters_0':
0,
'proventesters_-1':
0,
# for tracking number of types of karma
'1':
0,
'0':
0,
'-1':
0,
}
data = stats[release.name]
for status in statuses:
data['num_%s' % status] = PackageUpdate.select(
AND(PackageUpdate.q.releaseID == release.id,
PackageUpdate.q.status == status)).count()
for type in types:
data['num_%s' % type] = PackageUpdate.select(
AND(PackageUpdate.q.releaseID == release.id,
PackageUpdate.q.type == type)).count()
for update in release.updates:
for build in update.builds:
data['packages'][build.package] += 1
for bug in update.bugs:
data['bugs'].add(bug.bz_id)
feedback_done = False
testingtime_done = False
for comment in update.comments:
if comment.author_name in ('autoqa', 'taskotron'):
continue
# Track the # of +1's, -1's, and +0's.
if comment.author_name != 'bodhi':
data[str(comment.karma)] += 1
if comment.author_group == 'proventesters':
data['proventesters'].add(comment.author_name)
data['proventesters_%d' % comment.karma] += 1
if comment.text == 'This update has reached the stable karma threshold and will be pushed to the stable updates repository':
data['num_stablekarma'] += 1
elif comment.text and comment.text.endswith(
'days in testing and can be pushed to stable now if the maintainer wishes'
):
data['num_testingtime'] += 1
# For figuring out if an update has received feedback or not
if not feedback_done:
if (not comment.author.startswith('bodhi')
and comment.karma != 0 and not comment.anonymous):
data[
'num_feedback'] += 1 # per-release tracking of feedback
feedback += 1 # total number of updates that have received feedback
feedback_done = True # so we don't run this for each comment
# Tracking per-author karma & anonymous feedback
if not comment.author.startswith('bodhi'):
if comment.anonymous:
# @@: should we track anon +0 comments as "feedback"?
if comment.karma != 0:
data['num_anon_feedback'] += 1
else:
author = comment.author_name
data['karma'][author] += 1
karma[author] += 1
if (not testingtime_done and comment.text
== 'This update has been pushed to testing'):
for othercomment in update.comments:
if othercomment.text == 'This update has been pushed to stable':
delta = othercomment.timestamp - comment.timestamp
data['deltas'].append(delta)
data['occurrences'][delta.days] = \
data['occurrences'].setdefault(
delta.days, 0) + 1
data['accumulative'] += delta
testingtime_done = True
break
if update.critpath:
if update.critpath_approved or update.status == 'stable':
data['num_critpath_approved'] += 1
else:
if status in ('testing', 'pending'):
data['num_critpath_unapproved'] += 1
data['num_critpath'] += 1
#if not feedback_done:
if update.status == 'stable' and update.karma == 0:
data['critpath_without_karma'].add(update)
# Proventester metrics
proventester_karma = defaultdict(int) # {username: karma}
positive_proventesters = 0
negative_proventesters = 0
for comment in update.comments:
if comment.author_group == 'proventesters':
proventester_karma[
comment.author_name] += comment.karma
for _karma in proventester_karma.values():
if _karma > 0:
positive_proventesters += 1
elif _karma < 0:
negative_proventesters += 1
# Conflicting proventesters
if positive_proventesters and negative_proventesters:
data['conflicted_proventesters'] += [short_url(update)]
# Track updates with overall positive karma, including positive
# karma from a proventester
if update.karma > 0 and positive_proventesters:
data[
'critpath_positive_karma_including_proventesters'] += [
short_url(update)
]
# Track updates with overall positive karma, including negative
# karma from a proventester
if update.karma > 0 and negative_proventesters:
data['critpath_positive_karma_negative_proventesters'] += [
short_url(update)
]
if testingtime_done:
data['num_tested'] += 1
if not feedback_done:
data['num_tested_without_karma'] += 1
data['deltas'].sort()
print " * %d updates" % data['num_updates']
print " * %d packages updated" % (len(data['packages']))
for status in statuses:
print " * %d %s updates" % (data['num_%s' % status], status)
for type in types:
print " * %d %s updates (%0.2f%%)" % (
data['num_%s' % type], type,
float(data['num_%s' % type]) / data['num_updates'] * 100)
print " * %d bugs resolved" % len(data['bugs'])
print " * %d critical path updates (%0.2f%%)" % (
data['num_critpath'],
float(data['num_critpath']) / data['num_updates'] * 100)
print " * %d approved critical path updates" % (
data['num_critpath_approved'])
print " * %d unapproved critical path updates" % (
data['num_critpath_unapproved'])
print " * %d updates received feedback (%0.2f%%)" % (
data['num_feedback'],
(float(data['num_feedback']) / data['num_updates'] * 100))
print " * %d +0 comments" % data['0']
print " * %d +1 comments" % data['1']
print " * %d -1 comments" % data['-1']
print " * %d unique authenticated karma submitters" % (len(
data['karma']))
print " * %d proventesters" % len(data['proventesters'])
print " * %d +1's from proventesters" % data['proventesters_1']
print " * %d -1's from proventesters" % data['proventesters_-1']
if data['num_critpath']:
print " * %d critpath updates with conflicting proventesters (%0.2f%% of critpath)" % (
len(data['conflicted_proventesters']),
float(len(data['conflicted_proventesters'])) /
data['num_critpath'] * 100)
for u in data['conflicted_proventesters']:
print " * " + u
print " * %d critpath updates with positive karma and negative proventester feedback (%0.2f%% of critpath)" % (
len(data['critpath_positive_karma_negative_proventesters']),
float(
len(data['critpath_positive_karma_negative_proventesters'])
) / data['num_critpath'] * 100)
for u in data['critpath_positive_karma_negative_proventesters']:
print " * " + u
print " * %d critpath updates with positive karma and positive proventester feedback (%0.2f%% of critpath)" % (
len(data['critpath_positive_karma_including_proventesters']),
float(
len(data['critpath_positive_karma_including_proventesters']
)) / data['num_critpath'] * 100)
print " * %d anonymous users gave feedback (%0.2f%%)" % (
data['num_anon_feedback'], float(data['num_anon_feedback']) /
(data['num_anon_feedback'] + sum(data['karma'].values())) * 100)
# This does not take into account updates that reach stablekarma before being pushed to testing!
# print " * %d out of %d stable updates went through testing (%0.2f%%)" %(
# data['num_tested'], data['num_stable'],
# float(data['num_tested']) / data['num_stable'] * 100)
print " * %d updates reached the stable karma threshold (%0.2f%%)" % (
data['num_stablekarma'],
float(data['num_stablekarma']) / data['num_stable'] * 100)
print " * %d updates reached the minimum time in testing threshold (%0.2f%%)" % (
data['num_testingtime'],
float(data['num_testingtime']) / data['num_stable'] * 100)
print " * %d went from testing to stable *without* karma (%0.2f%%)" % (
data['num_tested_without_karma'],
float(data['num_tested_without_karma']) / data['num_tested'] * 100)
print " * %d updates were pushed to stable with negative karma (%0.2f%%)" % (
data['stable_with_negative_karma'],
float(data['stable_with_negative_karma']) / data['num_stable'] *
100)
print " * %d critical path updates pushed to stable *without* karma" % (
len(data['critpath_without_karma']))
#for update in data['critpath_without_karma']:
# print " * %s submitted by %s" % (update.title, update.submitter)
print " * Time spent in testing:"
print " * mean = %d days" % (data['accumulative'].days /
len(data['deltas']))
print " * median = %d days" % (data['deltas'][len(data['deltas']) /
2].days)
print " * mode = %d days" % (sorted(data['occurrences'].items(),
key=itemgetter(1))[-1][0])
#for package in sorted(data['packages'].items(), key=itemgetter(1), reverse=True):
# print " * %s: %d" % (package[0].name, package[1])
print
print
print "Out of %d total updates, %d received feedback (%0.2f%%)" % (
num_updates, feedback, (float(feedback) / num_updates * 100))
print "Out of %d total unique commenters, the top 50 are:" % (len(karma))
for submitter in sorted(karma.iteritems(), key=itemgetter(1),
reverse=True)[:50]:
print " * %s (%d)" % (submitter[0], submitter[1])
