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

arXiv: https://arxiv.org/abs/2407.09337 · HTML: https://mlsanity.com/paper/2407.09337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [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)
- [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)

## Built on by (most cited first, 1 of 1)

- [Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits](https://mlsanity.com/paper/2512.02898/) (2025)

## Abstract (arXiv)

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.