预测软件产品线中的特性缺陷

Rodrigo Queiroz, T. Berger, K. Czarnecki
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引用次数: 16

摘要

缺陷预测技术可以增强软件系统的质量保证活动。例如,它们可以用来预测源文件或函数中的错误。在软件产品线的上下文中,这种技术可以理想地用于预测功能或功能组合中的缺陷,这将允许开发人员将质量保证集中在容易出错的地方。在这个初步的案例研究中,我们研究了缺陷预测模型如何使用机器学习技术来识别缺陷特征。我们采用流程度量,并使用开源产品线评估和比较三个分类器。结果表明,该方法是有效的。我们的最佳场景在使用朴素贝叶斯分类器准确预测缺陷或清洁特征方面达到了73%的准确率。在此基础上,讨论了今后的工作方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards predicting feature defects in software product lines
Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in source files or functions. In the context of a software product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.
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