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

arXiv: https://arxiv.org/abs/2512.02898 · HTML: https://mlsanity.com/paper/2512.02898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems](https://mlsanity.com/paper/1812.07283/) (2018)
- [Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization](https://mlsanity.com/paper/2502.07786/) (2024)
- [Locating Faults with Program Slicing: An Empirical Analysis](https://mlsanity.com/paper/2101.03008/) (2021)
- [C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments](https://mlsanity.com/paper/2206.08768/) (2022)
- [InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments](https://mlsanity.com/paper/2206.14175/) (2022)
- [PyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C](https://mlsanity.com/paper/2508.08171/) (2025)
- [CFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases](https://mlsanity.com/paper/2407.09337/) (2024)
- [GitSEED: A Git-backed Automated Assessment Tool for Software Engineering and Programming Education](https://mlsanity.com/paper/2409.07362/) (2024)

## Abstract (arXiv)

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).