Empirical analysis of network measures for effort-aware fault-proneness prediction

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• 作者：Wanwangying Ma ; Lin Chen ; ^{lchen@nju.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Yibiao Yang ; Yuming Zhou ; Baowen Xu ; ^{bwxu@nju.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Network measures ; Dependency relationships ; Fault-proneness prediction ; Effort-aware
• 刊名：Information and Software Technology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：69
• 期：Complete
• 页码：50-70
• 全文大小：1802 K

文摘

Recently, network measures have been proposed to predict fault-prone modules. Leveraging the dependency relationships between software entities, network measures describe the structural features of software systems. However, there is no consensus about their effectiveness for fault-proneness prediction. Specifically, the predictive ability of network measures in effort-aware context has not been addressed.

Objective

We aim to provide a comprehensive evaluation on the predictive effectiveness of network measures with the effort needed to inspect the code taken into consideration.

Method

We first constructed software source code networks of 11 open-source projects by extracting the data and call dependencies between modules. We then employed univariate logistic regression to investigate how each single network measure was correlated with fault-proneness. Finally, we built multivariate prediction models to examine the usefulness of network measures under three prediction settings: cross-validation, across-release, and inter-project predictions. In particular, we used the effort-aware performance indicators to compare their predictive ability against the commonly used code metrics in both ranking and classification scenarios.

Results

Based on the 11 open-source software systems, our results show that: (1) most network measures are significantly positively related to fault-proneness; (2) the performance of network measures varies under different prediction settings; (3) network measures have inconsistent effects on various projects.

Conclusion

Network measures are of practical value in the context of effort-aware fault-proneness prediction, but researchers and practitioners should be careful of choosing whether and when to use network measures in practice.