Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2026

Abstract

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature review on learning-based dynamic fault localization from the aspects of the data task and the model task. Among them, each task is divided into multiple sub-tasks in a systematic manner to comprehensively discuss the details. In addition, we analyze and summarize the datasets and metrics that have been widely used to evaluate the effectiveness of the proposed techniques in recent years, so that researchers can have an intuitive perception of them. We also discuss the present challenges and the directions for future research.

Keywords

Fault localization, machine learning, deep learning, large language models, survey

Discipline

Artificial Intelligence and Robotics | Software Engineering

Publication

ACM Computing Surveys

Volume

58

Issue

9

First Page

1

Last Page

39

ISSN

0360-0300

Identifier

10.1145/3787202

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3787202

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