Effort-aware cross-project just-in-time defect prediction framework for mobile apps  

作　　者：Tian CHENG Kunsong ZHAO Song SUN Muhammad MATEEN Junhao WEN 

机构地区：[1]School of Big Data and Software Engineering,Chongqing University,Chongqing,401331,China [2]School of Computer Science,Wuhan University,Wuhan,430072,China [3]Department of Computer Science,Air University Multan Campus,Multan,60000,Pakistan

出　　处：《Frontiers of Computer Science》2022年第6期15-29,共15页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.62072060).

摘　　要：As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new requirements.Just-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile apps.As the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project model.In this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile apps.More specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features.Then,the adversarial learning technique is used to extract the common feature embedding for the model building.We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation.The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

关 键 词：kernel-based principal component analysis adversarial learning just-in-time defect prediction cross-project model 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]