BugLens: Leveraging Bisection for Lightweight Compiler Bug Deduplication

TL;DR

BugLens introduces a lightweight, bisection-based approach for compiler bug deduplication that significantly reduces human effort compared to existing analysis-heavy methods, demonstrating high practicality and effectiveness.

Contribution

The paper proposes BugLens, a novel deduplication method leveraging bisection and bug-triggering optimizations, offering a more practical and efficient alternative to traditional analysis-based techniques.

Findings

BugLens saves approximately 27% human effort in bug deduplication.

Bisection effectively locates failure-inducing commits for bug identification.

BugLens outperforms state-of-the-art methods Tamer and D3 in empirical evaluations.

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.