期刊
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
卷 11, 期 3, 页码 566-579出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETC.2023.3279388
关键词
Aging-related bug; network measures; software aging; software bug prediction
Software aging refers to the increasing failure rate or performance degradation in long-running software systems. Predicting aging-related bugs can help software teams allocate testing resources effectively. This study proposes aging-related network measures by constructing a dependency network to model the flow of aging-related information.
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.
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