On the Feasibility of Deduplicating Compiler Bugs with Bisection

• URL: http://arxiv.org/abs/2506.23281v1
• Date: Sun, 29 Jun 2025 15:12:57 GMT
• Title: On the Feasibility of Deduplicating Compiler Bugs with Bisection
• Authors: Xintong Zhou, Zhenyang Xu, Chengnian Sun,
• Abstract summary: Bug deduplication is a practical research problem known as bug deduplication.<n>Prior methodologies for compiler bug deduplication primarily rely on program analysis to extract bug-related features for duplicate identification.<n>We introduce BugLens, a novel deduplication method that primarily uses bisection, enhanced by the identification of bug-triggering optimizations to minimize false negatives.
• Score: 1.286741686995463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Random testing has proven to be an effective technique for compiler validation. However, the debugging of bugs identified through random testing presents a significant challenge due to the frequent occurrence of duplicate test programs that expose identical compiler bugs. The process to identify duplicates is a practical research problem known as bug deduplication. Prior methodologies for compiler bug deduplication primarily rely on program analysis to extract bug-related features for duplicate identification, which can result in substantial computational overhead and limited generalizability. This paper investigates the feasibility of employing bisection, a standard debugging procedure largely overlooked in prior research on compiler bug deduplication, for this purpose. Our study demonstrates that the utilization of bisection to locate failure-inducing commits provides a valuable criterion for deduplication, albeit one that requires supplementary techniques for more accurate identification. Building on these results, we introduce BugLens, a novel deduplication method that primarily uses bisection, enhanced by the identification of bug-triggering optimizations to minimize false negatives. Empirical evaluations conducted on four real-world datasets demonstrate that BugLens significantly outperforms the state-of-the-art analysis-based methodologies Tamer and D3 by saving an average of 26.98% and 9.64% human effort to identify the same number of distinct bugs. Given the inherent simplicity and generalizability of bisection, it presents a highly practical solution for compiler bug deduplication in real-world applications.

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