Thu 8 Nov 2018 10:30 - 10:52 at Horizons 5 - Estimation and Prediction Chair(s): Jim Herbsleb

Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above needs, we build a merged code change prediction tool. Our approach first extracts 34 features from code changes, which are grouped into 5 dimensions: code, file history, owner experience, collaboration network, and text. And then we leverage machine learning techniques such as random forest to build a prediction model. To evaluate the performance of our approach, we conduct experiments on three open source projects (i.e., Eclipse, LibreOffice, and OpenStack), containing a total of 166,215 code changes. Across three datasets, our approach statistically significantly improves random guess classifiers and two prediction models proposed by Jeong et al. (2009) and Gousios et al. (2014) in terms of several evaluation metrics. Besides, we also study the important features which distinguish merged code changes from abandoned ones.

Thu 8 Nov
Times are displayed in time zone: Guadalajara, Mexico City, Monterrey change

10:30 - 12:00
Estimation and PredictionResearch Papers / Journal-First at Horizons 5
Chair(s): Jim HerbslebCarnegie Mellon University
10:30
22m
Talk
Early prediction of merged code changes to prioritize reviewing tasks
Journal-First
Yuanrui Fan, Xin XiaMonash University, David LoSingapore Management University, Shanping Li
DOI
10:52
22m
Talk
How far we have progressed in the journey? An examination of cross-project defect prediction
Journal-First
Yuming Zhou, Yibiao YangNanjing University, China, Hongmin Lu, Lin ChenNanjing University, Yanhui Li, Yangyang Zhao, Junyan Qian, Baowen Xu
Link to publication DOI
11:15
22m
Talk
A Novel Automated Approach for Software Effort Estimation Based on Data Augmentation
Research Papers
11:37
22m
Talk
Predicting Node Failure in Cloud Service Systems
Research Papers
Qingwei LinMicrosoft, China, Ken Hsieh, Yingnong DangMicrosoft, USA, Hongyu ZhangThe University of Newcastle, Kaixin SuiMicrosoft, China, Yong XuMicrosoft, China, Jian-Guang LouMicrosoft Research, Chenggang LiNortheastern University, China, Youjiang WuMicrosoft, USA, Randolph YaoMicrosoft, USA, Murali ChintalapatiMicrosoft, USA, Dongmei ZhangMicrosoft Research, China