Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations

2025-12-27

AI Summary

Modern compilers like GCC pose significant challenges for fault localization, particularly in precisely attributing failures to source files. To address this, this paper proposes a fine-grained fault localization method based on adversarial compiler configuration pairs. The approach integrates spectrum-based fault localization (SBFL) with compiler-option-level control. Its key contributions are: (1) the first multi-pair generation mechanism producing “one-fail-one-pass” fine-grained adversarial configuration pairs; (2) lightweight failure-configuration construction and directed repair via passing-configuration derivation; and (3) configuration-pair-level independent ranking coupled with weighted voting aggregation. Evaluated on 60 real-world GCC bugs, the method achieves a Top-1 localization accuracy of 45.0% (27/60), outperforming Odfl and Basic by 35.0% and 28.6%, respectively. This demonstrates substantial improvements in both accuracy and robustness.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Compilers are fundamental to modern software development, making the effective identification and resolution of compiler faults essential. However, localizing these faults to specific source files remains highly challenging due to the complexity and scale of modern compiler infrastructures. In this study, we propose MultiConf, a novel approach that automatically isolates compiler faults by constructing multiple pairs of adversarial compilation configurations. Each adversarial compilation configuration pair consists of a failing configuration and its corresponding passing configuration, which differ in only a small number of fine-grained options. MultiConf generates failing configurations through a lightweight construction process and derives the corresponding passing configurations by selectively disabling bug-related fine-grained options. We then employ a Spectrum-Based Fault Localization (SBFL) formula to rank the suspiciousness of compiler source files. Each adversarial configuration pair independently produces a ranking, which is subsequently aggregated using a weighted voting scheme to derive a final suspiciousness ranking, enabling more accurate and robust fault localization. We evaluate MultiConf on a benchmark of 60 real-world GCC compiler bugs. The results demonstrate that MultiConf significantly outperforms existing compiler fault localization techniques in both effectiveness and efficiency. In particular, MultiConf successfully localizes 27 out of 60 bugs at the Top-1 file level, representing improvements of 35.0% and 28.6% over the two state-of-the-art approaches, Odfl(20) and Basic(21), respectively.

Problem

• Automatically isolates compiler faults using adversarial configurations
• Ranks suspicious source files via aggregated SBFL and voting
• Improves localization accuracy and efficiency for real-world GCC bugs

Innovation

• Multiple adversarial compilation configuration pairs isolate faults
• Lightweight construction and selective option disabling generate configurations
• Weighted voting aggregates SBFL rankings for accurate localization