Towards Automated Prediction of Software Bugs from Textual Description

Suyash Shukla, Sandeep Kumar

2023

Abstract

Every software deals with issues such as bugs, defect tracking, task management, development issue to a customer query, etc., in its entire lifecycle. An issue-tracking system (ITS) tracks issues and manages software development tasks. However, it has been noted that the inferred issue types often mismatch with the issue title and description. Recent studies showed machine learning (ML) based issue type prediction as a promising direction, mitigating manual issue type assignment problems. This work proposes an ensemble method for issue-type prediction using different ML classifiers. The effectiveness of the proposed model is evaluated over the 40302 manually validated issues of thirty-eight java projects from the SmartSHARK data repository, which has not been done earlier. The textual description of an issue is used as input to the classification model for predicting the type of issue. We employed the term frequency-inverse document frequency (TF-IDF) method to convert textual descriptions of issues into numerical features. We have compared the proposed approach with other widely used ensemble approaches and found that the proposed approach outperforms the other ensemble approaches with an accuracy of 81.41%. Further, we have compared the proposed approach with existing issue-type prediction models in the literature. The results show that the proposed approach performed better than existing models in the literature.

Paper Citation

in Harvard Style

Shukla S. and Kumar S. (2023). Towards Automated Prediction of Software Bugs from Textual Description. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 193-201. DOI: 10.5220/0011982400003464

in Bibtex Style

@conference{enase23,
author={Suyash Shukla and Sandeep Kumar},
title={Towards Automated Prediction of Software Bugs from Textual Description},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={193-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011982400003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Towards Automated Prediction of Software Bugs from Textual Description
SN - 978-989-758-647-7
AU - Shukla S.
AU - Kumar S.
PY - 2023
SP - 193
EP - 201
DO - 10.5220/0011982400003464
PB - SciTePress