IssueAssignmentWithTopicModeling

This repository contains all code and instructions required to reproduce the findings of the paper:

Themistoklis Diamantopoulos, Nikolaos Saoulidis, and Andreas Symeonidis
"Automated Issue Assignment using Topic Modeling on Jira Issue Tracking Data"
Paper submitted at the IET Software journal.

Instructions for running the code

Retrieving the data

First, you have to download the dataset found here.
Then, you must set the properties found in file properties.py.
After that, you can run script 1_mongo_get_and_save_data.py to retrieve the data.

Running the methodology

You can run the methodology by executing the files 2_features_preprocess_and_transform.py, 3_text_preprocessing.py, 4_prepare_train_test_sets.py, 5_apply_classification.py, 6_optimize_topics.py.
The results can be found in the data folder that you set up in the properties.py file as zip files. Each step produces different output files with the same numbers (e.g. step 3_text_preprocessing.py produces files of the form 3_{project_name}_{num_assignees}_assignees.csv).

Generating the tables and figures

You can run the scripts 7_1_evaluation_auc.py, 7_2_evaluation_num_assignees.py, 7_3_evaluation_assignees.py, 7_4_evaluation_labels.py, and 7_5_evaluation_classifiers.py to reproduce the tables and graphs shown in the paper.