Thu 8 Nov 2018 10:52 - 11:15 at Horizons 5 - Estimation and Prediction Chair(s): Jim Herbsleb

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 performance 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, ManualDown 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.

Thu 8 Nov

Displayed time zone: Guadalajara, Mexico City, Monterrey change

10:30 - 12:00
Estimation and PredictionResearch Papers / Journal-First at Horizons 5
Chair(s): Jim Herbsleb Carnegie Mellon University
10:30
22m
Talk
Early prediction of merged code changes to prioritize reviewing tasks
Journal-First
Yuanrui Fan , Xin Xia Monash University, David Lo Singapore 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 Yang Nanjing University, China, Hongmin Lu , Lin Chen Nanjing 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 Lin Microsoft, China, Ken Hsieh , Yingnong Dang Microsoft, USA, Hongyu Zhang The University of Newcastle, Kaixin Sui Microsoft, China, Yong Xu Microsoft, China, Jian-Guang Lou Microsoft Research, Chenggang Li Northeastern University, China, Youjiang Wu Microsoft, USA, Randolph Yao Microsoft, USA, Murali Chintalapati Microsoft, USA, Dongmei Zhang Microsoft Research, China