Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits

2025-12-02

AI Summary

Debugging multi-fault programs and circuits remains a critical bottleneck in software and hardware verification; existing formula-based fault localization (FBFL) techniques often lack diagnostic completeness and subset minimality. This paper introduces the first unified model-based diagnosis (MBD) framework for both C programs and Boolean circuits, which jointly encodes multiple failing test cases into a single MaxSAT problem to yield cross-observation-consistent, subset-minimal, and precise diagnoses. By innovatively integrating MBD theory with MaxSAT solving, our approach eliminates redundant diagnoses and enhances interpretability. Experimental evaluation on TCAS, C-Pack-IPAs, and ISCAS85 benchmarks demonstrates that CFaults significantly outperforms BugAssist, SNIPER, and HSD in fault localization speed for C programs while exclusively outputting minimal diagnosis sets; in circuit diagnosis, it matches HSD’s accuracy and efficiency.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Probabilistic Circuits and Graphical 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
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Debugging is one of the most time-consuming and expensive tasks in software development and circuit design. Several formula-based fault localisation (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/circuits with multiple faults. This paper introduces CFaults, a novel fault localisation tool for C software and Boolean circuits with multiple faults. CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and aggregates all failing test cases into a unified Maximum Satisfiability (MaxSAT) formula. Consequently, our method guarantees consistency across observations and simplifies the fault localisation procedure. Experimental results on three benchmark sets, two of C programs, TCAS and C-Pack-IPAs, and one of Boolean circuits, ISCAS85, show that CFaults is faster at localising faults in C software than other FBFL approaches such as BugAssist, SNIPER, and HSD. On the ISCAS85 benchmark, CFaults is generally slower than HSD; however, it localises faults in only 6% fewer circuits, demonstrating that it remains competitive in this domain. Furthermore, CFaults produces only subset-minimal diagnoses of faulty statements, whereas the other approaches tend to enumerate redundant diagnoses (e.g., BugAssist and SNIPER).

Problem

• Develops a unified fault localization tool for C software and Boolean circuits with multiple faults.
• Ensures consistent diagnoses across all failing tests using a unified MaxSAT formula.
• Produces only subset-minimal diagnoses, eliminating redundancy compared to existing methods.

Innovation

• Leverages Model-Based Diagnosis with multiple observations
• Aggregates failing tests into unified MaxSAT formula
• Guarantees subset-minimal diagnoses for C software and Boolean circuits