An empirical study on dependence clusters for effort-aware fault-proneness prediction
Book chapter
Yang, Yibiao, Harman, Mark, Krinke, Jens, Islam, S., Binkley, David, Zhou, Yuming and Xu, Baowen 2016. An empirical study on dependence clusters for effort-aware fault-proneness prediction. in: Lo, David, Apel, Sven and Khurshid, Sarfraz (ed.) ASE’16 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering IEEE/ACM. pp. 296-307
Authors | Yang, Yibiao, Harman, Mark, Krinke, Jens, Islam, S., Binkley, David, Zhou, Yuming and Xu, Baowen |
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Editors | Lo, David, Apel, Sven and Khurshid, Sarfraz |
Abstract | A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the function-level with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five well-known open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters influence software quality. |
Keywords | Dependence clusters; fault-proneness; fault prediction; network analysis |
Book title | ASE’16 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering |
Page range | 296-307 |
Year | 2016 |
Publisher | IEEE/ACM |
Publication dates | |
06 Oct 2016 | |
Publication process dates | |
Deposited | 02 Mar 2017 |
Event | 31st IEEE/ACM International Conference on Automated Software Engineering (ASE) |
ISBN | 978-1-4503-3845-5 |
978-1-5090-5571-5 | |
Funder | National Key Basic Research and Development Program of China |
National Natural Science Foundation of China | |
Natural Science Foundation of Jiangsu Province | |
Engineering and Physical Sciences Research Council (EPSRC) | |
National Science Foundation (NSF) | |
Fulbright award | |
National Key Basic Research and Development Program of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of China | |
National Natural Science Foundation of Jiangsu Province | |
Engineering and Physical Sciences Research Council | |
National Science Foundation | |
Fulbright award | |
Web address (URL) | http://ieeexplore.ieee.org/abstract/document/7582767/ |
Additional information | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Accepted author manuscript |
https://repository.uel.ac.uk/item/84z2x
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