Adopting Delta Maintainability Model for Just in Time Bug Prediction

Lerina Aversano, Martina Iammarino, Antonella Madau, Debora Montano, Chiara Verdone

2024

Abstract

A flaw that leads to a software malfunction is called a bug. Preventing bugs from the beginning reduces the need to address complex problems in later stages of development or after software release. Therefore, bug prevention helps create more stable and robust code because bug-free software is easier to maintain, update, and expand over time. In this regard, we propose a pipeline for the prevention of bugs in the source code, consisting of a machine learning model capable of predicting just in time whether a new commit inserted into the repository can be classified as ”good” or ”bad”. This is a critical issue as it directly affects the quality of our code. The approach is based on a set of features containing process software metrics at the commit level, some of which are related to the impact of changes. The proposed method was validated on data obtained from three open-source systems, for which the entire evolutionary history was considered, focusing mainly on those affected by bugs. The results are satisfactory and show not only the effectiveness of the proposed pipeline capable of working in continuous integration but also the ability of the approach to work cross-project, thus generalizing the results obtained.

Paper Citation

in Harvard Style

Aversano L., Iammarino M., Madau A., Montano D. and Verdone C. (2024). Adopting Delta Maintainability Model for Just in Time Bug Prediction. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 419-426. DOI: 10.5220/0012785100003753

in Bibtex Style

@conference{icsoft24,
author={Lerina Aversano and Martina Iammarino and Antonella Madau and Debora Montano and Chiara Verdone},
title={Adopting Delta Maintainability Model for Just in Time Bug Prediction},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={419-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012785100003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Adopting Delta Maintainability Model for Just in Time Bug Prediction
SN - 978-989-758-706-1
AU - Aversano L.
AU - Iammarino M.
AU - Madau A.
AU - Montano D.
AU - Verdone C.
PY - 2024
SP - 419
EP - 426
DO - 10.5220/0012785100003753
PB - SciTePress