A Commit Change-based Weighted Complex Network Approach to Identify Potential Fault Prone Classes

Chun Yong Chong, Sai Peck Lee

Abstract

Over the past few years, attention has been focused on utilizing complex network analysis to gain a high-level abstraction view of software systems. While many studies have been proposed to use interactions between software components at the variable, method, class, package, or combination of multiple levels, limited studies investigated how software change history and evolution pattern can be used as a basis to model software-based weighted complex network. This paper attempts to fill in the gap by proposing an approach to model a commit change-based weighted complex network based on historical software change and evolution data captured from GitHub repositories with the aim to identify potential fault prone classes. Experiments were carried out using three open-source software to validate the proposed approach. Using the well-known change burst metric as a benchmark, the proposed method achieved average precision of 0.77 and recall of 0.8 on all the three test subjects.

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Paper Citation


in Harvard Style

Yong Chong C. and Peck Lee S. (2018). A Commit Change-based Weighted Complex Network Approach to Identify Potential Fault Prone Classes.In Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-320-9, pages 437-448. DOI: 10.5220/0006828104370448


in Bibtex Style

@conference{icsoft18,
author={Chun Yong Chong and Sai Peck Lee},
title={A Commit Change-based Weighted Complex Network Approach to Identify Potential Fault Prone Classes},
booktitle={Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2018},
pages={437-448},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006828104370448},
isbn={978-989-758-320-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - A Commit Change-based Weighted Complex Network Approach to Identify Potential Fault Prone Classes
SN - 978-989-758-320-9
AU - Yong Chong C.
AU - Peck Lee S.
PY - 2018
SP - 437
EP - 448
DO - 10.5220/0006828104370448