A Developer Centered Bug Prediction Model

<pre>Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (i.e., size, complexity, etc.) and/or of the process adopted in their development and maintenance (i.e., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a ``hybrid'' prediction model combining our predictors with the state-of-the-art ones.</pre>

已有诸多技术被提出以实现软件缺陷（software defects）的精准预测。此类技术通常利用代码工件（code artefacts）的特征（如规模、复杂度等），以及其开发与维护过程中的相关特征（如负责某组件的开发人员数量），来识别存在缺陷风险的组件。尽管此类缺陷预测模型可达到较高的预测精度，但它们大多忽略了与人为相关的因素在缺陷引入过程中所发挥的重要作用。既往研究表明，工作专注力较强的开发人员相较于专注力较弱的开发人员，更不容易引入软件缺陷。基于这一观测结论，由专注力较强的开发人员修改的软件组件，其出现错误的概率也应低于由专注力较弱的开发人员修改的组件。我们通过度量参与某组件开发的开发人员所提交代码变更的分散程度，来量化这一观测结论，并基于此信息构建缺陷预测模型。我们在26个软件系统上对该模型进行了评估，并与四种具有竞争力的同类技术展开了对比实验。实验结果证实了本模型的优越性，且其与现有文献中常用的缺陷预测特征具备较高的互补性。基于上述实验结果，我们还展示了一种融合本研究提出的预测特征与当前主流预测特征的混合预测模型的实验效果。