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Characterization and localization of performance-bugs using Naive Bayes approach

机译:使用朴素贝叶斯方法对性能错误进行表征和本地化

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Performance of an application is a vital issue for user satisfaction. Performance bug refers to a specific kind of bugs that create lags and overheads in application execution. Often, it is difficult to localize and fix performance bugs due to insufficient developer knowledge regarding the characteristics of these bugs. Eliminating performance bugs manually from the application is time consuming and costly. This paper proposes a characterization and localization approach of performance bugs using Naive Bayes. It first detects the structures and behaviors responsible for the performance issues in source code. Then it learns the association of performance bugs with those coding characteristics from the training data set. Finally, it localizes potential performance bugs in source codes from the given bug report using Naive Bayes Classifier. Experimental result shows that our proposed approach can successfully predicts 75% of the performance bugs from various source codes.
机译:应用程序的性能对于用户满意度至关重要。性能错误是指一种特定类型的错误,会在应用程序执行过程中造成延迟和开销。通常,由于开发人员对这些错误特征的了解不足,因此很难对它们进行本地化和修复。从应用程序中手动消除性能错误既耗时又昂贵。本文提出了一种使用朴素贝叶斯的性能错误的表征和本地化方法。它首先检测源代码中导致性能问题的结构和行为。然后,它从训练数据集中学习性能错误与那些编码特征的关联。最后,它使用Naive Bayes分类器从给定的错误报告中定位源代码中潜在的性能错误。实验结果表明,我们提出的方法可以成功地从各种源代码中预测75%的性能错误。

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