The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-in-time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect-prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we propose a model, called Simplified Deep Forest (SDF), to conduct JIT defect prediction for Android mobile apps. SDF modifies a state-of-the-art deep forest model by removing the multigrained scanning operation that is designed for data with a high-dimensional feature space. It uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of 3 performance indicators.

中文翻译：

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基于简化深度森林模型的 Android 移动应用即时缺陷预测


移动设备的普及导致移动应用数量爆发式增长，其中Android移动应用为主流。 Android移动应用程序通常会因用户提出的新需求而频繁更新。即时 (JIT) 缺陷预测适用于这种质量保证场景，因为它可以通过确定新代码提交是否会将缺陷引入应用程序来提供及时反馈。由于缺陷预测性能通常依赖于数据表示的质量和所使用的分类模型，因此在这项工作中，我们提出了一种称为简化深度森林（SDF）的模型，用于对 Android 移动应用程序进行 JIT 缺陷预测。 SDF 通过删除专为具有高维特征空间的数据设计的多粒度扫描操作来修改最先进的深度森林模型。它使用具有集成森林的级联结构进行表示学习和分类。我们对 10 个 Android 移动应用程序进行了实验，实验结果表明，SDF 在 3 个性能指标方面均明显优于对比方法。