Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps
Kunsong Zhao, Zhou Xu, Tao Zhang, Yutian Tang, Meng Yan
Abstract
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.