SEPLJul 3

On the Feasibility of Deduplicating Compiler Bugs with Bisection

Xintong Zhou, Zhenyang Xu, Yongqiang Tian, Chengnian Sun
arXiv:2506.232819.41 citations
Predicted impact top 46% in SE · last 90 daysOriginality Incremental advance
AI Analysis

For compiler developers, this work provides a practical and generalizable approach to deduplicate bugs found via random testing, reducing manual effort.

The paper explores using bisection (finding failure-inducing commits) for compiler bug deduplication, and proposes BugLens which combines bisection with bug-triggering optimization identification. BugLens outperforms state-of-the-art methods Tamer and D3 by saving 33.56% and 10.68% human effort respectively on five real-world datasets.

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 five real-world datasets demonstrate that BugLens significantly outperforms the state-of-the-art analysis-based methodologies Tamer and D3 by saving an average of 33.56% and 10.68% 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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes