def main():
print("Loading data. This may take some time.")
records = [Record(project) for project in Project.available_projects()]
show_rates_table(records)
method_category_score(records)
method_score_distributions(records)
plt.show()
def main():
print('Loading data. This may take a while...')
projects = [(p.name, Record(p.descartes), Record(p.gregor)) for p in Project.available_projects()]
print('Execution time:')
time_table(projects)
plot_times(projects)
print('Number of mutants created:')
mutants_table(projects)
plot_mutants(projects)
plt.show()
def get_data():
for project in Project.available_projects():
record = Record(project)
non_accessible_methods = set(
method_id(m) for m in project.methods
if 'ACCESSIBLE' not in m['classifications'])
all_methods = set(method_id(m) for m in project.methods)
def ratio(a_set):
return len(a_set.intersection(non_accessible_methods)) / len(a_set)
yield (project.name, ratio(record.pseudo_tested),
ratio(record.methods_under_analysis), ratio(all_methods))
def main():
projects = list(Project.available_projects())
#Compute the scores
print('Computing the scores. This may take a while...')
scores = [get_both_scores(p) for p in projects]
descartes_scores = [c[0].score for c in scores]
gregor_scores = [c[1].score for c in scores]
#Show the table
render_table([p.name for p in projects], scores)
#Show the correlation
correlation = spearmanr(descartes_scores, gregor_scores)
print(f'The Spearman correlation coefficient is {correlation.correlation} with a p-value of {correlation.pvalue}')
#Show plot
show_plot(descartes_scores, gregor_scores)
bland_altman_plot(descartes_scores, gregor_scores)
plt.show()
def main():
show_probabilities(Project.available_projects())
