Toward More Efficient Statistical Debugging with Abstraction Refinement
Abstract
Debugging is known to be a notoriously painstaking and time-consuming task. As one major family of automated debugging, statistical debugging approaches have been well investigated over the past decade, which collect failing and passing executions and apply statistical techniques to identify discriminative elements as potential bug causes. Most of the existing approaches instrument the entire program to produce execution profiles for debugging, thus incurring hefty instrumentation and analysis cost. However, as in fact a major part of the program code is error-free, full-scale program instrumentation is wasteful and unnecessary.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 30, 2023
- Source ID
- 10.1145/3544790
Entities
People
- Chengnian Sun
- Harry Xu
- Lu Fang
- Shan Lu
- Siau Cheng Khoo
- Siyi Zhang
- Xintao Niu
- Zhiqiang Zuo
Organizations
- Nanjing University
- National Natural Science Foundation of China
- National Science Foundation
- National University of Singapore
- Natural Science Foundation of Jiangsu Province
- Office of Naval Research
- University of California
- University of California, Los Angeles
- University of Chicago
- University of Waterloo