How Far We Have Progressed in the Journey?An Examination of Cross-Project Defect Prediction YUMING ZHOU,YIBIAO YANG,HONGMIN LU,LIN CHEN,YANHUI LI,and YANGYANG ZHAO,Nanjing University JUNYAN QIAN,Guilin University of Electronic Technology BAOWEN XU,Nanjing University Background.Recent years have seen an increasing interest in cross-project defect prediction(CPDP),which aims to apply defect prediction models built on source projects to a target project.Currently,a variety of (complex)CPDP models have been proposed with a promising prediction performance. Problem.Most,if not all,of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective.We aim to investigate how far we have really progressed in the journey by comparing the perfor- mance in defect prediction between the existing CPDP models and simple module size models. Method.We first use module size in the target project to build two simple defect prediction models,Manual- Down and ManualUp,which do not require any training data from source projects.ManualDown considers a larger module as more defect-prone,while ManualUp considers a smaller module as more defect-prone. Then,we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models:using the same publicly available data sets,using the same performance indicators,and using the prediction performance reported in the original cross-project defect prediction studies. Result.The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature,including many newly proposed models. Conclusion.The results caution us that,if the prediction performance is the goal,the real progress in CPDP is not being achieved as it might have been envisaged.We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project. CCS Concepts:.Software and its engineering-Software evolution;Maintaining software; Additional Key Words and Phrases:Defect prediction,cross-project,supervised,unsupervised,model This work is partially supported by the National Key Basic Research and Development Program of China(2014CB340702) and the National Natural Science Foundation of China (61432001,61772259,61472175,61472178,61702256,61562015, 61403187). Authors'addresses:Y.Zhou (corresponding author),Y.Yang.H.Lu,L.Chen,Y.Li,Y.Zhao,and B.Xu(corresponding author), State Key Laboratory for Novel Software Technology,Nanjing University,No.163,Xianlin Road,Nanjing.210023,Jiangsu Province,P.R.China;emails:(zhouyuming.yangyibiao,hmlu,Ichen,yanhuili,bwxu@nju.edu.cn,csurjzhyy@163.com;Y. Qian,Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,541004,Guangxi Province,P.R.China;email:qjy2000@gmail.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.Copyrights for components of this work owned by others than ACM must be honored Abstracting with credit is permitted.To copy otherwise,or republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.Request permissions from permissions@acm.org. ©2018ACM1049-331X/2018/04-ART1$15.00 https:/∥doi.org/10.1145/3183339 ACM Transactions on Software Engineering and Methodology,Vol.27.No.1,Article 1.Pub.date:April 2018.1 How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction YUMING ZHOU, YIBIAO YANG, HONGMIN LU, LIN CHEN, YANHUI LI, and YANGYANG ZHAO, Nanjing University JUNYAN QIAN, Guilin University of Electronic Technology BAOWEN XU, Nanjing University Background. Recent years have seen an increasing interest in cross-project defect prediction (CPDP), which aims to apply defect prediction models built on source projects to a target project. Currently, a variety of (complex) CPDP models have been proposed with a promising prediction performance. Problem. Most, if not all, of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective. We aim to investigate how far we have really progressed in the journey by comparing the perfor￾mance in defect prediction between the existing CPDP models and simple module size models. Method. We first use module size in the target project to build two simple defect prediction models, Manual￾Down and ManualUp, which do not require any training data from source projects. ManualDown considers a larger module as more defect-prone, while ManualUp considers a smaller module as more defect-prone. Then, we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models: using the same publicly available data sets, using the same performance indicators, and using the prediction performance reported in the original cross-project defect prediction studies. Result. The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature, including many newly proposed models. Conclusion. The results caution us that, if the prediction performance is the goal, the real progress in CPDP is not being achieved as it might have been envisaged. We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project. CCS Concepts: • Software and its engineering → Software evolution; Maintaining software; Additional Key Words and Phrases: Defect prediction, cross-project, supervised, unsupervised, model This work is partially supported by the National Key Basic Research and Development Program of China (2014CB340702) and the National Natural Science Foundation of China (61432001, 61772259, 61472175, 61472178, 61702256, 61562015, 61403187). Authors’ addresses: Y. Zhou (corresponding author), Y. Yang, H. Lu, L. Chen, Y. Li, Y. Zhao, and B. Xu (corresponding author), State Key Laboratory for Novel Software Technology, Nanjing University, No. 163, Xianlin Road, Nanjing, 210023, Jiangsu Province, P.R. China; emails: {zhouyuming, yangyibiao, hmlu, lchen, yanhuili, bwxu}@nju.edu.cn, csurjzhyy@163.com; Y. Qian, Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi Province, P.R. China; email: qjy2000@gmail.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 ACM 1049-331X/2018/04-ART1 $15.00 https://doi.org/10.1145/3183339 ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018