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Cited by 337Software Testing and Debugging TechniquesSoftware Engineering ResearchSoftware System Performance and Reliability

The DOI matches its Crossref record; 84 of the 87 references it cites were found in a registry or an index.

DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

Open access
Li, Xia, Li, Wei, Zhang, Yuqun, Zhang, Lingming·2019·Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
DOI 10.1145/3293882.3330574
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Abstract

Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction.

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Metadata

Authors
Li, X., Li, W., Zhang, Y., & Zhang, L.
Affiliations
  • University of Texas at Dallas, USA
  • Southern University of Science and Technology (CN)
  • The University of Texas at Dallas (US)
ORCID
Venue
Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
Publisher
ACM
Published
2019-07-10
Pages
169-180
DOI
10.1145/3293882.3330574
OpenAlex
W2958754741
Cited by
336
References
87
Citations by year
2026: 48 · 2025: 62 · 2024: 57 · 2023: 47 · 2022: 51 · 2021: 42 · 2020: 20 · 2019: 9
FWCI
21.54
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (publishedVersion)
Funders
  • Shenzhen Peacock Plan (KQTD2016112514355531)
  • National Science Foundation (CCF-1566589,CCF-1763906)
  • Science and Technology Innovation Committee Foundation of Shenzhen (JCYJ20170817110848086)
Topics
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
Keywords
feature selection, latent feature learning, fault proneness, fault prediction, information retrieval, cross-project prediction, Defects4J, loss functions

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