Predicting Aging-Related Bugs Using Network Analysis on Aging-Related Dependency Networks

Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and remov...

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Bibliographic Details
Published inIEEE transactions on emerging topics in computing Vol. 11; no. 3; pp. 1 - 14
Main Authors Qin, Fangyun, Zheng, Zheng, Wan, Xiaohui, Liu, Zhihao, Shi, Zhiping
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Aging
Aging-related bug
Codes
Complexity theory
Computer bugs
Failure rates
Measurement
Modules
Network analysis
network measures
Performance degradation
Predictive models
Resource management
Software
software aging
software bug prediction
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Summary:Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and removal before software release, ARB prediction was proposed. Based on the prediction results, software teams can allocate limited testing resources to ARB-prone modules. Previous research has proposed several methods for both within-project and cross-project ARB prediction. However, they are based on the same set of metrics focusing on the contents of a single module, and only six metrics are aging-related. In this paper, we develop aging-related network measures by constructing an aging-related dependency network to model the flow of aging-related information in the software. Our evaluation on three commonly used open-source projects reveals that aging-related network measures show an inconsistent association with ARB-proneness in three projects, and the performance of aging-related network measures varies under different ARB prediction settings.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2023.3279388