def test_get_significant_terms_with_filter_positive(self):
issues = get_issues_dataframe(
fields=[
"Priority",
"Resolution",
"Description_tr",
"Assignee",
"Reporter",
],
filters=[{
"name": "Priority",
"filtration_type": "drop-down",
"current_value": ["Major"],
"exact_match": False,
}],
)
significant_terms = get_significant_terms(issues)
assert all([
significant_terms["chosen_metric"] == "Resolution Done",
significant_terms["metrics"] == [
"Resolution Done",
"Resolution Rejected",
"Resolution Unresolved",
"Priority Major",
],
])
def get(self, request):
cache = redis_conn.get(
f"user:{request.user.id}:analysis_and_training:filters"
)
filters = loads(cache) if cache else None
user = request.user
issues = get_issues_dataframe(
fields=[
get_source_field(user),
"Priority",
"Resolution",
"Description_tr",
"Assignee",
"Reporter",
],
filters=filters,
)
if issues.empty:
return Response({})
settings = {
"source_field": get_source_field(user),
"bug_resolution": get_bug_resolutions(user),
"mark_up_entities": get_mark_up_entities(user),
}
significant_terms = get_significant_terms(issues, settings)
context = {"significant_terms": significant_terms}
return Response(context)
def test_get_significant_terms(self):
issues = get_issues_dataframe(
fields=[
"Priority",
"Resolution",
"Description_tr",
"Assignee",
"Reporter",
],
filters=[],
)
significant_terms = get_significant_terms(issues)
assert significant_terms == self.result_significant_terms
def test_get_significant_terms_with_aot(self):
issues = get_issues_dataframe(
fields=[
"Priority",
"Resolution",
"Description_tr",
"Assignee",
"Reporter",
],
filters=[],
)
significant_terms = get_significant_terms(issues,
self.settings_for_aot)
result_significant_terms = self.result_significant_terms.copy()
result_significant_terms["metrics"].append("AOT1")
assert significant_terms == result_significant_terms
def get(self, request):
cache = redis_conn.get(f"analysis_and_training:{request.user.id}")
if cache:
return Response(loads(cache))
fields = get_issues_fields(request.user)
issues = get_issues(fields=fields)
if not issues:
# TODO: FE shows progress bar when data is empty
return Response({})
issues = pd.DataFrame.from_records(issues)
freq_terms = calculate_frequently_terms(issues)
statistics = calculate_statistics(
df=issues, series=["Comments", "Attachments", "Time to Resolve"])
defect_submission = calculate_defect_submission(df=issues,
period="Month")
significant_terms = get_significant_terms(issues)
filters = get_filters(request.user, issues=issues)
context = {
"records_count": {
"total": len(issues),
"filtered": len(issues)
},
"frequently_terms": freq_terms,
"statistics": statistics,
"submission_chart": defect_submission,
"significant_terms": significant_terms,
"filters": filters,
}
redis_conn.set(
name=f"analysis_and_training:{request.user.id}",
value=dumps(context),
ex=60 * 30,
)
return Response(context)
def test_get_significant_terms_with_filter_negative(self):
issues = get_issues_dataframe(
fields=[
"Priority",
"Resolution",
"Description_tr",
"Assignee",
"Reporter",
],
filters=[{
"name": "Priority",
"filtration_type": "drop-down",
"current_value": ["Critical"],
"exact_match": False,
}],
)
significant_terms = get_significant_terms(issues)
assert significant_terms == {
"metrics": [],
"chosen_metric": "",
"terms": {},
}
def post(self, request):
fields = get_issues_fields(request.user)
filters = get_filters(
request.user,
issues=pd.DataFrame.from_records(get_issues(fields=fields)),
)
if request.data.get("action") == "apply":
new_filters = request.data.get("filters")
if new_filters:
for new_filter in new_filters:
for filter_ in filters:
if new_filter["name"] == filter_["name"]:
filter_.update({
"current_value":
new_filter["current_value"],
"filtration_type":
new_filter["filtration_type"],
"exact_match":
new_filter["exact_match"],
})
issues = get_issues(filters=filters, fields=fields)
else:
issues = get_issues(fields=fields)
else:
issues = get_issues(fields=fields)
if len(issues) == 0:
context = {
"records_count": {
"total": get_issue_count(),
"filtered": 0
},
"frequently_terms": [],
"statistics": {},
"submission_chart": {},
"significant_terms": {},
"filters": filters,
}
redis_conn.set(f"analysis_and_training:{request.user.id}",
dumps(context))
return Response({})
issues = pd.DataFrame.from_records(issues)
freq_terms = calculate_frequently_terms(issues)
statistics = calculate_statistics(
df=issues, series=["Comments", "Attachments", "Time to Resolve"])
submission_chart = calculate_defect_submission(df=issues,
period="Month")
significant_terms = get_significant_terms(
issues, get_training_settings(request.user))
context = {
"records_count": {
"total": get_issue_count(),
"filtered": len(issues),
},
"frequently_terms": freq_terms,
"statistics": statistics,
"submission_chart": submission_chart,
"significant_terms": significant_terms,
"filters": filters,
}
redis_conn.set(f"analysis_and_training:{request.user.id}",
dumps(context))
return Response(context)