def _parse_issue(raw_issue: dict) -> dict:
"""Parse issue raw object structure.
Parameters:
----------
raw_issue:
Raw issue object to be parsed.
Returns:
----------
Returns object containing parsed issue attributes.
"""
issue = raw_issue.get("fields")
history = raw_issue.get("changelog").get("histories")
parsed_issue = {}
config_parser = configparser.ConfigParser()
config_parser.read(CONFIG_PARSE)
for new_field in config_parser["DEFAULT"]:
new_field = new_field.capitalize()
maps = config_parser["DEFAULT"][new_field].split(".")
func = FUNCTION_MAPPING.get(new_field)
value = (_get_value(maps, issue[maps[0]]) if maps[0] in issue
and issue[maps[0]] is not None else None)
if value is not None and func:
parsed_issue[new_field] = func(value)
else:
parsed_issue[new_field] = (
value
if value is not None else REPLACE_MAPPING[new_field]
if new_field in REPLACE_MAPPING.keys() else "")
parsed_issue["Key"] = raw_issue["key"] or ""
parsed_issue["History"] = [{
"Author":
snapshot.get("author").get("displayName")
if snapshot.get("author") else "",
"Created":
get_utc_datetime(snapshot.get("created"))
if snapshot.get("created") else "",
"Items": [{
"Field": item.get("field"),
"From": item.get("fromString"),
"To": item.get("toString"),
} for item in snapshot.get("items")]
if snapshot.get("items") else "",
} for snapshot in history]
parsed_issue["Description_tr"] = clean_text(
parsed_issue["Description"])
parsed_issue["Time to Resolve"] = calculate_ttr(
parsed_issue["Resolved"], parsed_issue["Created"])
for key in issue:
if key not in parsed_issue:
parsed_issue[key] = str(issue[key])
return parsed_issue
def post(self, request):
description = clean_text(request.data.get("description"))
if not description.strip():
raise CannotAnalyzeDescriptionWarning
training_parameters = get_training_parameters(request.user)
models = load_models(request.user)
probabilities = dict()
for parameter in training_parameters:
if parameter in ["Time to Resolve", "Priority"]:
probabilities[parameter] = get_probabilities(
description,
training_parameters[parameter],
models[parameter],
)
elif parameter == "Resolution":
probabilities["resolution"] = calculate_resolution_predictions(
description,
training_parameters[parameter],
models[parameter],
)
elif parameter == "areas_of_testing":
probabilities[
parameter] = calculate_area_of_testing_predictions(
description,
training_parameters[parameter],
models[parameter],
)
for probability in probabilities:
if probability == "resolution":
for resolution in probabilities[probability]:
resolution_obj = probabilities[probability][resolution]
for metric in resolution_obj:
resolution_obj[metric] = convert_to_integer(
resolution_obj[metric])
else:
for metric in probabilities[probability]:
probabilities[probability][metric] = convert_to_integer(
probabilities[probability][metric])
redis_conn.set(
f"user:{request.user.id}:description_assessment:description",
dumps(description),
)
redis_conn.set(
f"user:{request.user.id}:description_assessment:probabilities",
dumps(probabilities),
)
context = {"probabilities": probabilities}
return Response(context)
def post(self, request):
def _convert_to_integer(value):
return int(floor((value * 100) + 0.5))
description = clean_text(request.data.get("description"))
if not description.strip():
raise DescriptionCantAnalyzedWarning
archive_path = get_archive_path(request.user)
training_parameters = read_from_archive(
archive_path, TRAINING_PARAMETERS_FILENAME
)
probabilities = {}
probabilities["resolution"] = calculate_resolution_predictions(
description, training_parameters["Resolution"], archive_path
)
probabilities[
"areas_of_testing"
] = calculate_area_of_testing_predictions(
description, training_parameters["areas_of_testing"], archive_path
)
for metric in ["Time to Resolve", "Priority"]:
probabilities[metric] = get_probabilities(
description,
training_parameters[metric],
read_from_archive(archive_path, metric + ".sav"),
)
for probability in probabilities:
if probability == "resolution":
for resolution in probabilities[probability]:
resolution_obj = probabilities[probability][resolution]
for metric in resolution_obj:
resolution_obj[metric] = _convert_to_integer(
resolution_obj[metric]
)
else:
for metric in probabilities[probability]:
probabilities[probability][metric] = _convert_to_integer(
probabilities[probability][metric]
)
redis_conn.set(f"description:{request.user.id}", dumps(description))
redis_conn.set(
f"probabilities:{request.user.id}", dumps(probabilities)
)
context = {"probabilities": probabilities}
return Response(context)
def test_clean_text_expressions_basic(self):
text = r"[email protected] is test email. <head> is tag in html. http://test.com {Test.test}"
clear_text = clean_text(text)
assert clear_text == "is test email is tag html"
def test_clean_text_expressions_final(self):
text = "test for test +100500$\t s t "
clear_text = clean_text(text)
assert clear_text == "test test"
def test_clean_text_expressions_specific(self):
text = "Datetime 1234-12-12 21:21:21,123 "
clear_text = clean_text(text)
assert clear_text == "datetime"
def _parse_issue(raw_issue: dict) -> dict:
""" Parse issue raw object structure.
Parameters:
----------
raw_issue:
Raw issue object to be parsed.
Returns:
----------
Returns object containing parsed issue attributes.
"""
fields = raw_issue.get("fields")
history = raw_issue.get("changelog").get("histories")
parsed_issue = {
"Project":
fields["Project"].get("name")
if fields.get("Project") else "" or "",
"Attachments":
len(fields.get("Attachment") or []),
"Priority":
fields["Priority"].get("name")
if fields.get("Priority") else "" or "",
"Resolved":
get_utc_datetime(fields["Resolved"])
if fields.get("Resolved") else None,
"Updated":
get_utc_datetime(fields["Updated"])
if fields.get("Updated") else None,
"Labels":
",".join(fields["Labels"] or ""),
"Created":
get_utc_datetime(fields["Created"]),
"Comments":
len(fields["Comment"].get("comments") or []),
"Status":
fields["Status"]["name"],
"Key":
raw_issue["key"] or "",
"Summary":
fields["Summary"] or "",
"Resolution":
fields["Resolution"].get("name", "Unresolved")
if fields.get("Resolution") else "Unresolved",
"Description":
fields["Description"] or "",
"Components":
",".join([
component["name"] for component in fields["Component/s"]
]),
"Version":
",".join([el["name"] for el in fields["Versions"]])
if fields.get("Versions") else "",
"Assignee":
fields.get("Assignee").get("displayName")
if fields.get("Assignee") else "" or "",
"Reporter":
fields.get("Reporter").get("displayName")
if fields.get("Reporter") else "" or "",
"History": [{
"Author":
snapshot.get("author").get("displayName")
if snapshot.get("author") else "",
"Created":
get_utc_datetime(snapshot.get("created"))
if snapshot.get("created") else "",
"Items": [{
"Field": item.get("field"),
"From": item.get("fromString"),
"To": item.get("toString"),
} for item in snapshot.get("items")]
if snapshot.get("items") else "",
} for snapshot in history],
}
parsed_issue["Description_tr"] = clean_text(
parsed_issue["Description"])
parsed_issue["Time to Resolve"] = calculate_ttr(
parsed_issue["Resolved"], parsed_issue["Created"])
for key in fields:
if key not in parsed_issue:
parsed_issue[key] = str(fields[key])
return parsed_issue