Central Concerns and Questions

With various integrated development environments, developer productivities have been improved dramatically. However, testing and debugging processes are still very costly and require much manual effort. Improving the efficacy and efficiency of testing and debugging processes is critical for improving the overall productivity of software development.

Emerging Ideas and Initiatives

We tackle the following questions and aim to provide automated solutions for testing and debugging by leveraging the advances in the area of programming languages, software engineering, data mining and machine learning: (1) How to effectively expose different types of software failures? (2) How to accurately locate root causes of software failures? (3) How to facilitate easy understanding of the failures and causes? (4) How to generate possible fixes for the failures? (5) How to leverage knowledge about previous defects and fixes to prevent similar ones?

Selected Publications

[[1] Hong Cheng, David Lo , Yang Zhou, Xiaoyin Wang, and Xifeng Yan. Identifying Bug Signatures using Discriminative Graph Mining. Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'09), pages 141-152, Chicago, Illinois, USA. July 20-24, 2009. ACM.

[2] David Lo , Hong Cheng, Jiawei Han, Siau-Cheng Khoo, and Chengnian Sun. Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach. Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'09), pages 557-566, Paris, France. June 28-July 1, 2009. ACM.

[3] Lingxiao Jiang and Zhendong Su. Profile-Guided Program Simplification for Effective Testing and Analysis. Proceedings of the 16th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE'08), pages 48-58, Atlanta, Georgia, USA, November 9-14, 2008. ACM.

[4] Lingxiao Jiang and Zhendong Su. Context-Aware Statistical Debugging: From Bug Predictors to Faulty Control Flow Paths. Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE'07), pages 184-193, Atlanta, Georgia, USA, November 5-9, 2007. ACM.

Projects, Presentations and Posters

1. Lingxiao Jiang and Zhendong Su. Profile-Guided Program Simplification for Effective Testing and Analysis (slides)
2. ingxiao Jiang and Zhendong Su. Context-Aware Statistical Debugging: From Bug Predictors to Faulty Control Flow Paths (slides)

Collaborations and Industry Linkages

1. Department of Systems Engineering & Engineering Management, Chinese University of Hong Kong
2. Computer Science Department, University of California at Santa Barbara