Bug prediction is helpful for facilitating bug fixes and improving the efficiency in software development and maintenance. In the past decades, researchers have proposed numerous studies on bug prediction by using cod...
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ISBN:
(纸本)9781665448970
Bug prediction is helpful for facilitating bug fixes and improving the efficiency in software development and maintenance. In the past decades, researchers have proposed numerous studies on bug prediction by using codemetrics. However, most of the existing studies use syntax-based metrics, there exists little work building bug prediction models with semanticmetrics from source code. In this paper, we propose a new model, semantic dependency graph (SDG), to represent semantic relationships among source files. Based on the SDG, we formally define a suite of semanticmetrics reflecting semantic characteristics of a project's source files. Moreover, we create a tool to automate the generation of our proposed SDG-based metrics. Through our experimental studies, we have demonstrated that the SDG-based semanticmetrics are effective for building bug prediction models, and the SDG-based metrics outperform traditional syntactic metrics on bug prediction. In addition, models using the SDG-based metrics could achieve a better prediction performance than two state-of-the-art models that learn semantic features automatically. Finally, we have also presented that our approach is applicable in practice in terms of execution time and space.
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