Academic Journal
Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction
| Title: | Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction |
|---|---|
| Authors: | Li, Y., Wong, W. Eric, Lee, Shou-Yu, Wotawa, F. |
| Contributors: | Wong, W. Eric, Lee, Shou-Yu, 65810187 (Wong, WE), 0000-0002-1021-4753 (Wong, WE) |
| Source: | IEEE Access |
| Publisher Information: | Institute of Electrical and Electronics Engineers Inc. |
| Publication Year: | 2019 |
| Subject Terms: | Online social networks, Computer software--Design and construction |
| Description: | Software modules and developers are two core elements during the process of software development. Previous studies have shown that analyzing relations between 1) software modules; (2) developers; and (3) modules and developers, is critical to understand how they interact with each other, which ultimately affects software quality. Specifically, relations such as developer contribution relation, module dependency relation, and developer collaboration relation have been used independently or in pairs to build networks for software fault-proneness prediction. However, none of them investigate the joint effort of these three relations. Bearing this in mind, in this paper, we propose a tri-relation network, a weighted network that integrates developer contribution, module dependency, and developer collaboration relations to study their combined impact on software quality. Four network node centrality metrics are further derived from the proposed network to predict the fault-proneness of a given software module at the file level. Moreover, we have explored a mechanism to refine current networks in order to further improve the effectiveness of software fault-proneness prediction. © 2013 IEEE. ; Erik Jonsson School of Engineering and Computer Science |
| Document Type: | article in journal/newspaper |
| File Description: | application/pdf |
| Language: | English |
| Relation: | http://dx.doi.org/10.1109/ACCESS.2019.2916615; https://hdl.handle.net/10735.1/8662 |
| DOI: | 10.1109/ACCESS.2019.2916615 |
| Availability: | https://hdl.handle.net/10735.1/8662 https://doi.org/10.1109/ACCESS.2019.2916615 |
| Rights: | CC BY 4.0 (Attribution) ; ©2019 The Authors ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.4E9B3474 |
| Database: | BASE |
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