# Fault Localization with Code Coverage Representation Learning

arXiv: https://arxiv.org/abs/2103.00270 · HTML: https://mlsanity.com/paper/2103.00270/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2021.

## Built on by (most cited first, 16 of 16)

- [Large Language Models for Test-Free Fault Localization](https://mlsanity.com/paper/2310.01726/) (2023)
- [UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging](https://mlsanity.com/paper/2404.17153/) (2024)
- [Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis](https://mlsanity.com/paper/2509.13782/) (2025)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Tool-integrated Reinforcement Learning for Repo Deep Search](https://mlsanity.com/paper/2508.03012/) (2025)
- [ChatDBG: Augmenting Debugging with Large Language Models](https://mlsanity.com/paper/2403.16354/) (2024)
- [A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion](https://mlsanity.com/paper/2409.13642/) (2024)
- [A Library for Representing Python Programs as Graphs for Machine Learning](https://mlsanity.com/paper/2208.07461/) (2022)
- [Multi-Granularity Detector for Vulnerability Fixes](https://mlsanity.com/paper/2305.13884/) (2023)
- [ARIST: An Effective API Argument Recommendation Approach](https://mlsanity.com/paper/2306.06620/) (2023)
- [Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning](https://mlsanity.com/paper/2301.08442/) (2023)
- [Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces](https://mlsanity.com/paper/2501.18005/) (2025)
- [Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks](https://mlsanity.com/paper/2406.11437/) (2024)
- [Aligning Programming Language and Natural Language: Exploring Design Choices in Multi-Modal Transformer-Based Embedding for Bug Localization](https://mlsanity.com/paper/2406.17615/) (2024)
- [LLM-Based Automated Diagnosis Of Integration Test Failures At Google](https://mlsanity.com/paper/2604.12108/) (2026)
- [PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents](https://mlsanity.com/paper/2605.08468/) (2026)

## Abstract (arXiv)

In this paper, we propose DeepRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DeepRL4FL does so via novel code coverage representation learning (RL) and data dependencies RL for program statements. Those two types of RL on the dynamic information in a code coverage matrix are also combined with the code representation learning on the static information of the usual suspicious source code. This combination is inspired by crime scene investigation in which investigators analyze the crime scene (failed test cases and statements) and related persons (statements with dependencies), and at the same time, examine the usual suspects who have committed a similar crime in the past (similar buggy code in the training data). For the code coverage information, DeepRL4FL first orders the test cases and marks error-exhibiting code statements, expecting that a model can recognize the patterns discriminating between faulty and non-faulty statements/methods. For dependencies among statements, the suspiciousness of a statement is seen taking into account the data dependencies to other statements in execution and data flows, in addition to the statement by itself. Finally, the vector representations for code coverage matrix, data dependencies among statements, and source code are combined and used as the input of a classifier built from a Convolution Neural Network to detect buggy statements/methods. Our empirical evaluation shows that DeepRL4FL improves the top-1 results over the state-of-the-art statement-level FL baselines from 173.1% to 491.7%. It also improves the top-1 results over the existing method-level FL baselines from 15.0% to 206.3%.