# Simple Fault Localization using Execution Traces

arXiv: https://arxiv.org/abs/2503.04301 · HTML: https://mlsanity.com/paper/2503.04301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [On the Role of Fault Localization Context for LLM-Based Program Repair](https://mlsanity.com/paper/2604.05481/) (2026)
- [SemLoc: Structured Grounding of Free-Form LLM Reasoning for Fault Localization](https://mlsanity.com/paper/2603.29109/) (2026)

## Abstract (arXiv)

Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provide a wealth of additional information beyond mere coverage. Working with thousands of execution traces of short programs submitted to competitive programming contests and leveraging machine learning and additional runtime, control-flow and lexical features, we present simple ways to improve SBFL. We also propose a simple trick to integrate context information. Our approach outperforms SBFL formulae such as Ochiai on our evaluation set as well as QuixBugs and requires neither a GPU nor any form of advanced program analysis. Existing SBFL solutions could possibly be improved with reasonable effort by adopting some of the proposed ideas.