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DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization
Open accessAbstract
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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CounterFault: Value-Based Fault Localization by Modeling and Predicting Counterfactual Outcomes
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HMBFL: Higher-Order Mutation-Based Fault Localization
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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)
- Venue
- Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2019-07-10
- Pages
- 169-180
- 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
- bronze
- doi.org
- 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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