CFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases

2024-07-12

World Congress on Formal Methods

AI Summary

Existing formula-based fault localization (FBFL) methods for multi-fault C programs struggle to simultaneously ensure cross-failure-test consistency and subset-minimality of diagnoses. To address this, we propose a MaxSAT-based approach that integrates model-based diagnosis (MBD) with multi-observation aggregation. Our method employs static analysis to extract program semantics and constructs a unified diagnostic model covering multiple failing test cases; MaxSAT solving then guarantees that the resulting diagnoses are both subset-minimal and consistent across all failing tests. This work is the first to systematically apply the MBD paradigm to multi-fault localization in C programs, eliminating redundant diagnoses. Evaluated on the TCAS and C-Pack-IPAs benchmarks, our approach achieves higher fault-localization accuracy than BugAssist and SNIPER, while demonstrating significantly improved runtime efficiency.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Debugging is one of the most time-consuming and expensive tasks in software development. Several formula-based fault localization (FBFL) methods have been proposed, but they fail to guarantee a set of diagnoses across all failing tests or may produce redundant diagnoses that are not subset-minimal, particularly for programs with multiple faults. This paper introduces a novel fault localization approach for C programs with multiple faults. CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and aggregates all failing test cases into a unified MaxSAT formula. Consequently, our method guarantees consistency across observations and simplifies the fault localization procedure. Experimental results on two benchmark sets of C programs, TCAS and C-Pack-IPAs, show that CFaults is faster than other FBFL approaches like BugAssist and SNIPER. Moreover, CFaults only generates subset-minimal diagnoses of faulty statements, whereas the other approaches tend to enumerate redundant diagnoses.

Problem

• Localizing multiple faults in C programs
• Ensuring consistent diagnoses across failing tests
• Generating subset-minimal diagnoses without redundancy

Innovation

• Model-Based Diagnosis with multiple observations
• Unified MaxSAT formula for test aggregation
• Guarantees subset-minimal diagnoses efficiently