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 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Self-citationLearning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
A statistical interpretation of term specificity and its application in retrieval
Jones, K. S. (2021) · J. Documentation · doi:10.1108/eb026526 · Cited by 5,541
Research works printed but not linked: 12
- S. Planning , “ The economic impacts of inadequate infrastructure for software testing ,” 2002 . S. Planning, “The economic impacts of inadequate infrastructure for software testing,” 2002.
- R. Abreu , P. Zoeteweij , and A. J. Van Gemund , “ On the accuracy of spectrumbased fault localization ,” in Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION, 2007 . TAICPART-MUTATION 2007 . IEEE, 2007, pp. 89– 98 . R. Abreu, P. Zoeteweij, and A. J. Van Gemund, “On the accuracy of spectrumbased fault localization,” in Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION, 2007. TAICPART-MUTATION 2007. IEEE, 2007, pp. 89–98.
- 10.5555/2337223.2337225 · DOI
- “ Tensorflow website ,” 2018 . {Online}. Available: https://www.tensorflow.org/ “Tensorflow website,” 2018. {Online}. Available: https://www.tensorflow.org/
- S. Kim , C. Le Goues , M. Pradel , and A. Roychoudhury , “ Automated program repair (dagstuhl seminar 17022) ,” in Dagstuhl Reports , vol. 7 , no. 1. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017. S. Kim, C. Le Goues, M. Pradel, and A. Roychoudhury, “Automated program repair (dagstuhl seminar 17022),” in Dagstuhl Reports, vol. 7, no. 1. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017.
- “ Asm java bytecode manipulation and analysis framework ,” 2018 . {Online}. Available: http://asm.ow2.org/ “Asm java bytecode manipulation and analysis framework,” 2018. {Online}. Available: http://asm.ow2.org/
- “ Pit mutation testing system ,” 2018 . {Online}. Available: http://pitest.org/ “Pit mutation testing system,” 2018. {Online}. Available: http://pitest.org/
- “ Jhawk website ,” 2018 . {Online}. Available: http://www.virtualmachinery.com/ jhawkprod.htm “Jhawk website,” 2018. {Online}. Available: http://www.virtualmachinery.com/ jhawkprod.htm
- “ Indri website ,” 2018 . {Online}. Available: https://www.lemurproject.org/indri. php “Indri website,” 2018. {Online}. Available: https://www.lemurproject.org/indri. php
- 10.5555/3155562.3155642 · DOI
- Y. LeCun , C. Cortes , and C. Burges , “ Mnist handwritten digit database ,” AT&T Labs {Online}. Available: http://yann. lecun. com/exdb/mnist , vol. 2 , 2010 . Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database,” AT&T Labs {Online}. Available: http://yann. lecun. com/exdb/mnist, vol. 2, 2010.
- “ Effect size ,” 2016 . {Online}. Available: https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC5122517/ “Effect size,” 2016. {Online}. Available: https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC5122517/
Cited by
Sorted by citations
Less training, more repairing please: revisiting automated program repair via zero-shot learning
Xia, C. S., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549101 · Cited by 231
Self-citationLarge Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
Kang, S., Yoon, J., & Yoo, S. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00194 · Cited by 204
Deep learning library testing via effective model generation
Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3409761 · Cited by 167
Practical program repair via bytecode mutation
Ghanbari, A., Benton, S., & Zhang, L. (2019) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3293882.3330559 · Cited by 174
Self-citationWu, H., Zhang, Z., Wang, S., Yan, L., Lin, B., Qin, Y., Zhang, H., & Mao, X. (2021) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre52982.2021.00047 · Cited by 173
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
