Subsequent releases of a system have common development environments and characteristics. However, prediction models based on within-project data potentially suffer from being based on fault data reported within relatively short maintenance time intervals, which potentially decreases their prediction abilities. In this study, the authors propose an approach that improves the classification performance of models based on within-project data that are applied to predict the fault-proneness of the classes in a software post-release (PR). The proposed approach involves selecting a set of immediate pre-releases and constructing a prediction model based on each pre-release. The PR classes are categorised based on whether they are newly developed or they are reused, with or without modification, from one or more of the selected pre-releases. The prediction models are applied to the PR classes reused from selected pre-releases, and the results are used to construct a fault-proneness prediction model. After applying this prediction model to all PR classes, the fault-proneness results are adjusted by considering the relationship between the prediction results of the individual pre-release models and the actual fault data. They reported an empirical study that shows that the classification performance of the categorisation-based fault-proneness prediction models is considerably better than those constructed using existing approaches.

中文翻译：

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基于类别的方法来预测软件发布后的面向对象类的故障倾向

系统的后续发行版具有共同的开发环境和特征。但是，基于项目内数据的预测模型可能会基于在相对较短的维护时间间隔内报告的故障数据，从而可能降低其预测能力。在这项研究中，作者提出了一种基于项目内数据提高模型分类性能的方法，这些数据可用于预测软件发布后（PR）中类的故障倾向。提议的方法涉及选择一组立即预发布，并基于每个预发布构建预测模型。根据PR类是从一个或多个选定的预发行版本中重新开发的，还是经过重新修改（无论是否经过修改）而分类的。将预测模型应用于从选定的预发行版重用的PR类，并将结果用于构建故障倾向性预测模型。在将此预测模型应用于所有PR类之后，通过考虑各个预发布模型的预测结果与实际故障数据之间的关系来调整故障倾向性结果。他们报告了一项经验研究，该研究表明基于分类的故障倾向性预测模型的分类性能明显优于使用现有方法构建的模型。通过考虑各个预发布模型的预测结果与实际故障数据之间的关系来调整故障倾向性结果。他们报告了一项经验研究，该研究表明基于分类的故障倾向性预测模型的分类性能明显优于使用现有方法构建的模型。通过考虑各个预发布模型的预测结果与实际故障数据之间的关系来调整故障倾向性结果。他们报告了一项经验研究，该研究表明基于分类的故障倾向性预测模型的分类性能明显优于使用现有方法构建的模型。