Paper Info
Title
Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis
Abstract
Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB prediction is proposed as a solution to build the target project's ARB predictor by using the labeled data from the source project. A key point for cross-project ARB prediction is to reduce distribution difference between source and target project. However, existing approaches mainly focus on the marginal distribution difference while somehow overlook the conditional distribution difference, and they mainly use random oversampling to alleviate the class imbalance which may lead to overfitting. To address these problems, we propose a new crossproject ARB prediction approach based on Joint Distribution Adaptation (JDA) and Improved Subclass Discriminant Analysis (ISDA), called JDA-ISDA. The key idea of JDA-ISDA is first to use JDA to reduce the marginal distribution and conditional distribution difference jointly and then apply ISDA to alleviate the severe class imbalance problem. A set of experiments are carried out on two large open-source projects with six different machine learning (ML) classifiers. The experimental results demonstrate that compared with the state-of-the-art Transfer Learning based Aging-related bug Prediction (TLAP) and Supervised Representation Learning Approach (SRLA), JDA-ISDA is much more robust to different ML classifiers than TLAP, and the average improvement in terms of the balance value can be achieved up to 31.8%, and JDA-ISDA also outperforms TLAP and SRLA on average when logistic regression is chosen as the classifier for best performance prediction.
Year
DOI
Venue
2020
10.1109/ISSRE5003.2020.00038
2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)
Keywords
DocType
ISSN
software aging,aging-related bugs,cross-project ARB prediction,joint distribution adaptation,improved subclass discriminant analysis
Conference
1071-9458
ISBN
Citations 
PageRank 
978-1-7281-9871-2
0
0.34
References 
Authors
30
6
Name
Order
Citations
PageRank
Bin Xu131.05
Dongdong Zhao23420.62
Kai Jia300.34
Junwei Zhou411816.64
Jing Tian5426.49
Jianwen Xiang657.86