def get_parts(self, class_name, story):
case = class_name.split()
means_compounds = []
means_compounds.append(story.means.main_object.compound)
ends_compounds = story.ends.compounds
if story.means.free_form:
if len(story.means.compounds) > 0:
if type(story.means.compounds[0]) is list:
mc = [item for item in sublist for sublist in story.means.compounds]
else:
mc = story.means.compounds
means_compounds.extend(mc)
if len(ends_compounds) > 0:
if type(ends_compounds[0]) is list:
ends_compounds = [item for item in sublist for sublist in story.ends.compounds]
role = []
means = []
ends = []
rme = []
for token in story.data:
if token in story.role.text:
if len(case) != 1:
role.append(NLPUtility.case(token))
elif token not in story.role.functional_role.compound:
role.append(NLPUtility.case(token))
if token in story.means.text:
if len(case) != 1:
means.append(NLPUtility.case(token))
elif token not in means_compounds:
means.append(NLPUtility.case(token))
if story.has_ends:
if token in story.ends.text:
if len(case) != 1:
ends.append(NLPUtility.case(token))
elif token not in ends_compounds:
ends.append(NLPUtility.case(token))
if Utility.is_sublist(case, role):
rme.append('Role')
if Utility.is_sublist(case, means):
rme.append('Means')
if Utility.is_sublist(case, ends):
rme.append('Ends')
return rme
def to_stats_array(stories): stats = [] sent_stats = [] if stories: header = ['US_ID', 'User_Story', 'Words', 'Verbs', 'Nouns', 'NPs', 'Ind_R', 'Ind_M', 'Ind_E', 'FR_Type', 'MV_Type', 'DO_Type'] stats.append(header) sent_header = ['US_ID', 'Role_NP', 'Role_Struct', 'Role_Struct_Detail', 'Means_NP', 'Means_Struct', 'Means_Struct_Detail', 'Ends_NP', 'Ends_Struct', 'Ends_Struct_Detail'] sent_stats.append(sent_header) for us in stories: stats.append([us.number, us.text, us.stats.words, us.stats.verbs, us.stats.nouns, us.stats.noun_phrases, us.stats.indicators.role, us.stats.indicators.means, us.stats.indicators.ends, us.stats.fr_type, us.stats.mv_type, us.stats.do_type]) sent_stats.append([us.number, Utility.text(us.stats.role.nps), Utility.text(us.stats.role.general), Utility.text(us.stats.role.detail), Utility.text(us.stats.means.nps), Utility.text(us.stats.means.general), Utility.text(us.stats.means.detail), Utility.text(us.stats.means.nps), Utility.text(us.stats.means.general), Utility.text(us.stats.means.detail)]) return stats, sent_stats
def get_noun_phrases(story, span, part='data'):
phrases = []
for chunk in eval('story.' + str(part) + '.noun_chunks'):
chunk = MinerUtility.get_span(story, chunk)
if Utility.is_sublist(chunk, span):
phrases.append(MinerUtility.get_span(story, chunk))
return phrases
def get_noun_phrases(story, span, part='data'):
phrases = []
for chunk in eval('story.' + str(part) + '.noun_chunks'):
chunk = MinerUtility.get_span(story, chunk)
if Utility.is_sublist(chunk, span):
phrases.append(MinerUtility.get_span(story, chunk))
return phrases
def parse(text, id, systemname, nlp, miner): """Create a new user story object and mines it to map all data in the user story text to a predefined model :param text: The user story text :param id: The user story ID, which can later be used to identify the user story :param systemname: Name of the system this user story belongs to :param nlp: Natural Language Processor (spaCy) :param miner: instance of class Miner :returns: A new user story object """ no_punct = Utility.remove_punct(text) no_double_space = ' '.join(no_punct.split()) doc = nlp(no_double_space) user_story = UserStory(id, text, no_double_space) user_story.system.main = nlp(systemname)[0] user_story.data = doc #Printer.print_dependencies(user_story) #Printer.print_noun_phrases(user_story) miner.structure(user_story) user_story.old_data = user_story.data user_story.data = nlp(user_story.sentence) miner.mine(user_story, nlp) return user_story
def parse(text, id, systemname, nlp, miner):
"""Create a new user story object and mines it to map all data in the user story text to a predefined model
:param text: The user story text
:param id: The user story ID, which can later be used to identify the user story
:param systemname: Name of the system this user story belongs to
:param nlp: Natural Language Processor (spaCy)
:param miner: instance of class Miner
:returns: A new user story object
"""
no_punct = Utility.remove_punct(text)
no_double_space = ' '.join(no_punct.split())
doc = nlp(no_double_space)
user_story = UserStory(id, text, no_double_space)
user_story.system.main = nlp(systemname)[0]
user_story.data = doc
#Printer.print_dependencies(user_story)
#Printer.print_noun_phrases(user_story)
miner.structure(user_story)
user_story.old_data = user_story.data
user_story.data = nlp(user_story.sentence)
miner.mine(user_story, nlp)
return user_story
def to_stats_array(stories):
stats = []
sent_stats = []
if stories:
header = [
"US_ID",
"User_Story",
"Words",
"Verbs",
"Nouns",
"NPs",
"Ind_R",
"Ind_M",
"Ind_E",
"FR_Type",
"MV_Type",
"DO_Type",
]
stats.append(header)
sent_header = [
"US_ID",
"Role_NP",
"Role_Struct",
"Role_Struct_Detail",
"Means_NP",
"Means_Struct",
"Means_Struct_Detail",
"Ends_NP",
"Ends_Struct",
"Ends_Struct_Detail",
]
sent_stats.append(sent_header)
for us in stories:
stats.append(
[
us.number,
us.text,
us.stats.words,
us.stats.verbs,
us.stats.nouns,
us.stats.noun_phrases,
us.stats.indicators.role,
us.stats.indicators.means,
us.stats.indicators.ends,
us.stats.fr_type,
us.stats.mv_type,
us.stats.do_type,
]
)
sent_stats.append(
[
us.number,
Utility.text(us.stats.role.nps),
Utility.text(us.stats.role.general),
Utility.text(us.stats.role.detail),
Utility.text(us.stats.means.nps),
Utility.text(us.stats.means.general),
Utility.text(us.stats.means.detail),
Utility.text(us.stats.means.nps),
Utility.text(us.stats.means.general),
Utility.text(us.stats.means.detail),
]
)
return stats, sent_stats
def main(filename, systemname, print_us, print_ont, statistics, link, prolog, per_role, threshold, base, weights):
"""General class to run the entire program
"""
# Initialize spaCy just once (this takes most of the time...)
print("Initializing Natural Language Processor . . .")
start_nlp_time = timeit.default_timer()
nlp = English()
nlp_time = timeit.default_timer() - start_nlp_time
start_parse_time = timeit.default_timer()
miner = StoryMiner()
# Read the input file
set = Reader.parse(filename)
us_id = 1
# Keep track of all errors
success = 0
fail = 0
list_of_fails = []
errors = ""
c = Counter()
# Keeps track of all succesfully created User Stories objects
us_instances = []
failed_stories = []
success_stories = []
# Parse every user story (remove punctuation and mine)
for s in set:
try:
user_story = parse(s, us_id, systemname, nlp, miner)
user_story = c.count(user_story)
success = success + 1
us_instances.append(user_story)
success_stories.append(s)
except ValueError as err:
failed_stories.append([us_id, s, err.args])
errors += "\n[User Story " + str(us_id) + " ERROR] " + str(err.args[0]) + "! (\"" + " ".join(str.split(s)) + "\")"
fail = fail + 1
us_id = us_id + 1
# Print errors (if found)
if errors:
Printer.print_head("PARSING ERRORS")
print(errors)
parse_time = timeit.default_timer() - start_parse_time
# Generate the term-by-user story matrix (m), and additional data in two other matrices
start_matr_time = timeit.default_timer()
matrix = Matrix(base, weights)
matrices = matrix.generate(us_instances, ' '.join(success_stories), nlp)
m = matrices[0]
count_matrix = matrices[1]
stories_list = matrices[2]
rme = matrices[3]
matr_time = timeit.default_timer() - start_matr_time
# Print details per user story, if argument '-u'/'--print_us' is chosen
if print_us:
print("Details:\n")
for us in us_instances:
Printer.print_us_data(us)
# Generate the ontology
start_gen_time = timeit.default_timer()
patterns = Constructor(nlp, us_instances, m)
out = patterns.make(systemname, threshold, link)
output_ontology = out[0]
output_prolog = out[1]
output_ontobj = out[2]
output_prologobj = out[3]
onto_per_role = out[4]
# Print out the ontology in the terminal, if argument '-o'/'--print_ont' is chosen
if print_ont:
Printer.print_head("MANCHESTER OWL")
print(output_ontology)
gen_time = timeit.default_timer() - start_gen_time
# Gather statistics and print the results
stats_time = 0
if statistics:
start_stats_time = timeit.default_timer()
statsarr = Statistics.to_stats_array(us_instances)
Printer.print_head("USER STORY STATISTICS")
Printer.print_stats(statsarr[0], True)
Printer.print_stats(statsarr[1], True)
Printer.print_subhead("Term - by - User Story Matrix ( Terms w/ total weight 0 hidden )")
hide_zero = m[(m['sum'] > 0)]
print(hide_zero)
stats_time = timeit.default_timer() - start_stats_time
# Write output files
w = Writer()
folder = "output/" + str(systemname)
reports_folder = folder + "/reports"
stats_folder = reports_folder + "/stats"
outputfile = w.make_file(folder + "/ontology", str(systemname), "omn", output_ontology)
files = [["Manchester Ontology", outputfile]]
outputcsv = ""
sent_outputcsv = ""
matrixcsv = ""
if statistics:
outputcsv = w.make_file(stats_folder, str(systemname), "csv", statsarr[0])
matrixcsv = w.make_file(stats_folder, str(systemname) + "-term_by_US_matrix", "csv", m)
sent_outputcsv = w.make_file(stats_folder, str(systemname) + "-sentences", "csv", statsarr[1])
files.append(["General statistics", outputcsv])
files.append(["Term-by-User Story matrix", matrixcsv])
files.append(["Sentence statistics", sent_outputcsv])
if prolog:
outputpl = w.make_file(folder + "/prolog", str(systemname), "pl", output_prolog)
files.append(["Prolog", outputpl])
if per_role:
for o in onto_per_role:
name = str(systemname) + "-" + str(o[0])
pont = w.make_file(folder + "/ontology", name, "omn", o[1])
files.append(["Individual Ontology for '" + str(o[0]) + "'", pont])
# Print the used ontology generation settings
Printer.print_gen_settings(matrix, base, threshold)
# Print details of the generation
Printer.print_details(fail, success, nlp_time, parse_time, matr_time, gen_time, stats_time)
report_dict = {
"stories": us_instances,
"failed_stories": failed_stories,
"systemname": systemname,
"us_success": success,
"us_fail": fail,
"times": [["Initializing Natural Language Processor (<em>spaCy</em> v" + pkg_resources.get_distribution("spacy").version + ")" , nlp_time], ["Mining User Stories", parse_time], ["Creating Factor Matrix", matr_time], ["Generating Manchester Ontology", gen_time], ["Gathering statistics", stats_time]],
"dir": os.path.dirname(os.path.realpath(__file__)),
"inputfile": filename,
"inputfile_lines": len(set),
"outputfiles": files,
"threshold": threshold,
"base": base,
"matrix": matrix,
"weights": m['sum'].copy().reset_index().sort_values(['sum'], ascending=False).values.tolist(),
"counts": count_matrix.reset_index().values.tolist(),
"classes": output_ontobj.classes,
"relationships": output_prologobj.relationships,
"types": list(count_matrix.columns.values),
"ontology": Utility.multiline(output_ontology)
}
# Finally, generate a report
report = w.make_file(reports_folder, str(systemname) + "_REPORT", "html", generate_report(report_dict))
files.append(["Report", report])
# Print the location and name of all output files
for file in files:
if str(file[1]) != "":
print(str(file[0]) + " file succesfully created at: \"" + str(file[1]) + "\"")
def main(filename, systemname, print_us, print_ont, statistics, link, prolog,
per_role, threshold, base, weights):
"""General class to run the entire program
"""
# Initialize spaCy just once (this takes most of the time...)
print("Initializing Natural Language Processor . . .")
start_nlp_time = timeit.default_timer()
nlp = English()
nlp_time = timeit.default_timer() - start_nlp_time
start_parse_time = timeit.default_timer()
miner = StoryMiner()
# Read the input file
set = Reader.parse(filename)
us_id = 1
# Keep track of all errors
success = 0
fail = 0
list_of_fails = []
errors = ""
c = Counter()
# Keeps track of all succesfully created User Stories objects
us_instances = []
failed_stories = []
success_stories = []
# Parse every user story (remove punctuation and mine)
for s in set:
try:
user_story = parse(s, us_id, systemname, nlp, miner)
user_story = c.count(user_story)
success = success + 1
us_instances.append(user_story)
success_stories.append(s)
except ValueError as err:
failed_stories.append([us_id, s, err.args])
errors += "\n[User Story " + str(us_id) + " ERROR] " + str(
err.args[0]) + "! (\"" + " ".join(str.split(s)) + "\")"
fail = fail + 1
us_id = us_id + 1
# Print errors (if found)
if errors:
Printer.print_head("PARSING ERRORS")
print(errors)
parse_time = timeit.default_timer() - start_parse_time
# Generate the term-by-user story matrix (m), and additional data in two other matrices
start_matr_time = timeit.default_timer()
matrix = Matrix(base, weights)
matrices = matrix.generate(us_instances, ' '.join(success_stories), nlp)
m = matrices[0]
count_matrix = matrices[1]
stories_list = matrices[2]
rme = matrices[3]
matr_time = timeit.default_timer() - start_matr_time
# Print details per user story, if argument '-u'/'--print_us' is chosen
if print_us:
print("Details:\n")
for us in us_instances:
Printer.print_us_data(us)
# Generate the ontology
start_gen_time = timeit.default_timer()
patterns = Constructor(nlp, us_instances, m)
out = patterns.make(systemname, threshold, link)
output_ontology = out[0]
output_prolog = out[1]
output_ontobj = out[2]
output_prologobj = out[3]
onto_per_role = out[4]
# Print out the ontology in the terminal, if argument '-o'/'--print_ont' is chosen
if print_ont:
Printer.print_head("MANCHESTER OWL")
print(output_ontology)
gen_time = timeit.default_timer() - start_gen_time
# Gather statistics and print the results
stats_time = 0
if statistics:
start_stats_time = timeit.default_timer()
statsarr = Statistics.to_stats_array(us_instances)
Printer.print_head("USER STORY STATISTICS")
Printer.print_stats(statsarr[0], True)
Printer.print_stats(statsarr[1], True)
Printer.print_subhead(
"Term - by - User Story Matrix ( Terms w/ total weight 0 hidden )")
hide_zero = m[(m['sum'] > 0)]
print(hide_zero)
stats_time = timeit.default_timer() - start_stats_time
# Write output files
w = Writer()
folder = "output/" + str(systemname)
reports_folder = folder + "/reports"
stats_folder = reports_folder + "/stats"
outputfile = w.make_file(folder + "/ontology", str(systemname), "omn",
output_ontology)
files = [["Manchester Ontology", outputfile]]
outputcsv = ""
sent_outputcsv = ""
matrixcsv = ""
if statistics:
outputcsv = w.make_file(stats_folder, str(systemname), "csv",
statsarr[0])
matrixcsv = w.make_file(stats_folder,
str(systemname) + "-term_by_US_matrix", "csv",
m)
sent_outputcsv = w.make_file(stats_folder,
str(systemname) + "-sentences", "csv",
statsarr[1])
files.append(["General statistics", outputcsv])
files.append(["Term-by-User Story matrix", matrixcsv])
files.append(["Sentence statistics", sent_outputcsv])
if prolog:
outputpl = w.make_file(folder + "/prolog", str(systemname), "pl",
output_prolog)
files.append(["Prolog", outputpl])
if per_role:
for o in onto_per_role:
name = str(systemname) + "-" + str(o[0])
pont = w.make_file(folder + "/ontology", name, "omn", o[1])
files.append(["Individual Ontology for '" + str(o[0]) + "'", pont])
# Print the used ontology generation settings
Printer.print_gen_settings(matrix, base, threshold)
# Print details of the generation
Printer.print_details(fail, success, nlp_time, parse_time, matr_time,
gen_time, stats_time)
report_dict = {
"stories":
us_instances,
"failed_stories":
failed_stories,
"systemname":
systemname,
"us_success":
success,
"us_fail":
fail,
"times": [[
"Initializing Natural Language Processor (<em>spaCy</em> v" +
pkg_resources.get_distribution("spacy").version + ")", nlp_time
], ["Mining User Stories", parse_time],
["Creating Factor Matrix", matr_time],
["Generating Manchester Ontology", gen_time],
["Gathering statistics", stats_time]],
"dir":
os.path.dirname(os.path.realpath(__file__)),
"inputfile":
filename,
"inputfile_lines":
len(set),
"outputfiles":
files,
"threshold":
threshold,
"base":
base,
"matrix":
matrix,
"weights":
m['sum'].copy().reset_index().sort_values(
['sum'], ascending=False).values.tolist(),
"counts":
count_matrix.reset_index().values.tolist(),
"classes":
output_ontobj.classes,
"relationships":
output_prologobj.relationships,
"types":
list(count_matrix.columns.values),
"ontology":
Utility.multiline(output_ontology)
}
# Finally, generate a report
report = w.make_file(reports_folder,
str(systemname) + "_REPORT", "html",
generate_report(report_dict))
files.append(["Report", report])
# Print the location and name of all output files
for file in files:
if str(file[1]) != "":
print(
str(file[0]) + " file succesfully created at: \"" +
str(file[1]) + "\"")
