Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912743720747008 |
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| author | Orvalho, Pedro Kwiatkowska, Marta Janota, Mikoláš Manquinho, Vasco |
| author_facet | Orvalho, Pedro Kwiatkowska, Marta Janota, Mikoláš Manquinho, Vasco |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits Orvalho, Pedro Kwiatkowska, Marta Janota, Mikoláš Manquinho, Vasco Software Engineering Artificial Intelligence Logic in Computer Science Symbolic Computation 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). |
| title | Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits |
| topic | Software Engineering Artificial Intelligence Logic in Computer Science Symbolic Computation |
| url | https://arxiv.org/abs/2512.02898 |