Silent compiler bug de-duplication via three-dimensional analysis

• Main Authors: YANG, Chen, CHEN, Junjie, FAN, Xingyu, JIANG, Jiajun, SUN, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Software and its engineering; Software creation and management; Software verification and validation; Software defect analysis; Software testing and debugging; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8149; https://ink.library.smu.edu.sg/context/sis_research/article/9152/viewcontent/3597926.3598087.pdf
• Institution: Singapore Management University

Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs from the testing process and identify three-dimensional information (i.e., test program, optimizations, and test execution) for bug de-duplication. However, there are huge amount of bug-irrelevant details on the three dimensions, D3 then systematically conducts causal analysis to identify bug-causal features from each of the three dimensions for more accurate bug de-duplication. Finally, D3 ranks the test failures that are more likely to be caused by different silent bugs higher by measuring the distance among test failures based on the three-dimensional bug-causal features. Our experimental results on four datasets (including duplicate bugs of both GCC and LLVM) demonstrate the significant superiority of D3 over the two state-of-the-art compiler bug de-duplication techniques, achieving the average improvement of 19.36% and 51.43% in identifying unique silent compiler bugs when analyzing the same number of test failures.