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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“Moreover, learning-based techniques have also been proposed to leverage machine learning to combine multiple sources of dynamic/static information, including DeepFL [Li et al. 2019], FLUCCS [Sohn and Yoo 2017], and TRANSFER [Meng et al. 2022].”
Self-citationFuzzing deep-learning libraries via automated relational API inference
Deng, Y., Yang, C., Wei, A., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549085 · Cited by 86 · see citing works
Self-citationInferring Program Transformations From Singular Examples via Big Code
Jiang, J., Ren, L., Xiong, Y., & Zhang, L. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00033 · Cited by 77 · see citing works
Self-citationGao, X. Y., Wang, B., Duck, G. J., Ji, R., Xiong, Y., & Roychoudhury, A. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3418461 · Cited by 72 · see citing works
Actionable and interpretable fault localization for recurring failures in online service systems
Li, Z., Zhao, N., Li, M., Lu, X., Wang, L., Chang, D., Nie, X., Cao, L., Zhang, W., Sui, K., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549092 · Cited by 75 · see citing works
Explainable automated debugging via large language model-driven scientific debugging
Kang, S., Chen, B., Yoo, S., & Lou, J. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10594-x · Cited by 72 · see citing works
Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach
Zhang, Z., Lei, Y., Yan, M. L., Yu, Y., Chen, J., Wang, S., & Mao, X. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3560428 · Cited by 69 · see citing works
A universal data augmentation approach for fault localization
Xie, H., Lei, Y., Yan, M. L., Yu, Y., Xia, X., & Mao, X. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510136 · Cited by 67 · see citing works
A study of effectiveness of deep learning in locating real faults
Zhang, Z., Lei, Y., Mao, X., Yan, M. L., Xu, L., & Zhang, X. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106486 · Cited by 64 · see citing works
Combining Spectrum-Based Fault Localization and Statistical Debugging: An Empirical Study
Jiang, J., Wang, R., Xiong, Y., Chen, X., & Zhang, L. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00054 · Cited by 63 · see citing works
Self-citationOn the Reproducibility and Replicability of Deep Learning in Software Engineering
Liu, C. C., Gao, C., Xia, X., Lo, D., Grundy, J., & Yang, X. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3477535 · Cited by 64 · see citing works
Compiler bug isolation via effective witness test program generation
Chen, J., Han, J., Sun, P., Zhang, L., Hao, D., & Zhang, L. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3338957 · Cited by 60 · see citing works
Self-citationVELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Ding, Y., Suneja, S., Zheng, Y., Laredo, J., Morari, A., Kaiser, G. E., & Ray, B. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00114 · Cited by 57 · see citing works
Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques
Kucuk, Y., Henderson, T. A. D., & Podgurski, A. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00066 · Cited by 55 · see citing works
Fault localization via efficient probabilistic modeling of program semantics
Zeng, M., Wu, Y., Ye, Z., Xiong, Y., Zhang, X., & Zhang, L. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510073 · Cited by 44 · see citing works
Self-citationHistory-driven build failure fixing: how far are we?
Lou, Y., Chen, J., Zhang, L., Hao, D., & Zhang, L. (2019) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3293882.3330578 · Cited by 44 · see citing works
Self-citationLearning to Construct Better Mutation Faults
Tian, Z., Chen, J., Zhu, Q., Yang, J., & Zhang, L. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3556949 · Cited by 43 · see citing works
Self-citationEnhanced compiler bug isolation via memoized search
Chen, J., Ma, H., & Zhang, L. (2020) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3324884.3416570 · Cited by 44 · see citing works
Self-citationThe Plastic Surgery Hypothesis in the Era of Large Language Models
Xia, C. S., Ding, Y., & Zhang, L. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00047 · Cited by 36 · see citing works
Self-citationExtracting Concise Bug-Fixing Patches from Human-Written Patches in Version Control Systems
Jiang, Y., Liu, H., Niu, N., Zhang, L., & Hu, Y. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00069 · Cited by 41 · see citing works
Self-citationFuzzing Automatic Differentiation in Deep-Learning Libraries
Yang, C., Deng, Y., Yao, J., Tu, Y., Li, H., & Zhang, L. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00105 · Cited by 45 · see citing works
Self-citationGNet4FL: effective fault localization via graph convolutional neural network
Qian, J., Ju, X., & Chen, X. (2023) · Automated Software Engineering · doi:10.1007/s10515-023-00383-z · Cited by 37 · see citing works
PraPR: Practical Program Repair via Bytecode Mutation
Ghanbari, A., & Zhang, L. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00116 · Cited by 37 · see citing works
Self-citationContext-Aware Neural Fault Localization
Zhang, Z., Lei, Y., Mao, X., Yan, M. L., Xia, X., & Lo, D. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3279125 · Cited by 36 · see citing works
On the effectiveness of unified debugging
Benton, S., Li, X., Lou, Y., & Zhang, L. (2020) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3324884.3416566 · Cited by 34 · see citing works
Self-citationSoapFL: A Standard Operating Procedure for LLM-Based Method-Level Fault Localization
Qin, Y., Wang, S., Lou, Y., Dong, J., Wang, K., Li, X., & Mao, X. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3543187 · Cited by 32 · see citing works
Self-citationCall Frequency-Based Fault Localization
Vancsics, B., Horváth, F., Szatmári, A., & Beszédes, Á. (2021) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner50967.2021.00041 · Cited by 31 · see citing works
Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations
Applis, L., Panichella, A., & Arie Van Deursen, A. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678706 · Cited by 30 · see citing works
Fast and Precise On-the-Fly Patch Validation for All
Chen, L., Ouyang, Y., & Zhang, L. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00104 · Cited by 29 · see citing works
Self-citationNeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
Tokui, S., Tokumoto, S., Yoshii, A., Ishikawa, F., Nakagawa, T., Munakata, K., & Kikuchi, S. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00128 · Cited by 29 · see citing works
Can Higher-Order Mutants Improve the Performance of Mutation-Based Fault Localization?
Wang, H., Li, Z., Liu, Y., Chen, X., Doyle, P., Cai, Y., & Fan, L. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3162039 · Cited by 29 · see citing works
Feature-FL: Feature-Based Fault Localization
Lei, Y., Xie, H., Zhang, T., Yan, M. L., Xu, Z., & Sun, C. P. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3140453 · Cited by 28 · see citing works
Wu, M., Ouyang, Y., Zhou, H., Zhang, L., Liu, C., & Zhang, Y. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380358 · Cited by 28 · see citing works
Self-citationBetter Automatic Program Repair by Using Bug Reports and Tests Together
Motwani, M., & Brun, Y. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00109 · Cited by 27 · see citing works
Efficient algorithms for task mapping on heterogeneous CPU/GPU platforms for fast completion time
Li, Z., Zhang, Y., Ding, A., Zhou, H., & Liu, C. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101936 · Cited by 25 · see citing works
Self-citationFault localization to detect co-change fixing locations
Li, Y., Wang, S., & Nguyen, T. N. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549137 · Cited by 25 · see citing works
Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation
Rafi, M. N., Kim, D. J., Chen, A. R., Chen, T.-H. (., & Wang, S. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660793 · Cited by 24 · see citing works
Localizing Vulnerabilities Statistically From One Exploit
Shen, S., Kolluri, A., Dong, Z., Saxena, P., & Roychoudhury, A. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3437528 · Cited by 25 · see citing works
Combi-FL: Neural network and SBFL based fault localization using mutation analysis
Dutta, A., Srivastava, S. S., Godboley, S., & Mohapatra, D. P. (2021) · Journal of Computer Languages · doi:10.1016/j.cola.2021.101064 · Cited by 24 · see citing works
Predicting Economic Growth by Region Embedding: A Multigraph Convolutional Network Approach
Hui, B., Yan, D., Ku, W., & Wang, W. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411882 · Cited by 23 · see citing works
A Large-scale Study on API Misuses in the Wild
Li, X., Jiang, J., Benton, S., Xiong, Y., & Zhang, L. (2021) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst49551.2021.00034 · Cited by 23 · see citing works
Self-citationSoftware engineering meets deep learning
Ferreira, F., Silva, L. L., & Valente, M. T. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3442029 · Cited by 23 · see citing works
Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models
Wang, Z., Zhou, Z., Song, D., Huang, Y., Chen, S., Ma, L., & Zhang, T. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00180 · Cited by 40 · see citing works
BugBuilder: An Automated Approach to Building Bug Repository
Jiang, Y., Liu, H., Luo, X., Zhu, Z., Chi, X., Niu, N., Zhang, Y., Hu, Y., Bian, P., & Zhang, L. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3177713 · Cited by 21 · see citing works
Self-citationModeling function-level interactions for file-level bug localization
Liang, H., Hang, D., & Li, X. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10237-z · Cited by 21 · see citing works
Self-citationAdaptive Search-based Repair of Deep Neural Networks
Calsi, D. L., Duran, M., Laurent, T., Zhang, X., Arcaini, P., & Ishikawa, F. (2023) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3583131.3590477 · Cited by 20 · see citing works
Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction
Callaghan, D., & Fischer, B. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598148 · Cited by 20 · see citing works
Automating CUDA Synchronization via Program Transformation
Wu, M., Zhang, L., Liu, C., Tan, S. H., & Zhang, Y. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00075 · Cited by 20 · see citing works
Self-citationAgentless: Demystifying LLM-based Software Engineering Agents
Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2024) · arXiv · doi:10.48550/arxiv.2407.01489 · Cited by 24 · see citing works
“Moreover, learning-based techniques have also been proposed to leverage machine learning to combine multiple sources of dynamic/static information, including DeepFL [54], FLUCCS [86], and TRANSFER [67].”
MethodSelf-citationFlexFL: Flexible and Effective Fault Localization With Open-Source Large Language Models
Xu, C., Liu, Z., Ren, X., Zhang, G., Liang, M., & Lo, D. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3553363 · Cited by 25 · see citing works
Automated patching for unreproducible builds
Ren, Z., Sun, S., Xuan, J., Li, X., Zhou, Z., & Jiang, H. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510102 · Cited by 19 · see citing works
Self-citationEvaluating and Improving Unified Debugging
Benton, S., Li, X., Lou, Y., & Zhang, L. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3125203 · Cited by 19 · see citing works
Self-citationDeFort: Automatic Detection and Analysis of Price Manipulation Attacks in DeFi Applications
Xie, M., Hu, M., Kong, Z., Zhang, C., Feng, Y., Wang, H., Xue, Y., Zhang, H., Liu, Y., & Liu, Y. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3652137 · Cited by 18 · see citing works
Software Fault Localization: an Overview of Research, Techniques, and Tools
Wong, W. E., Gao, R., Li, Y., Abreu, R., Wotawa, F., & Li, D. (2023) · doi:10.1002/9781119880929.ch1 · Cited by 18 · see citing works
Deep Just-In-Time Defect Localization
Qiu, F., Gao, Z., Xia, X., Lo, D., Grundy, J., & Wang, X. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3135875 · Cited by 17 · see citing works
BCL-FL: A Data Augmentation Approach with Between-Class Learning for Fault Localization
Yan, L., Liu, C., Xie, H., Huang, S., Yan, M. L., & Xu, Z. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00045 · Cited by 17 · see citing works
A Light-Weight Data Augmentation Method for Fault Localization
Hu, J., Xie, H., Lei, Y., & Yu, K. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2023.107148 · Cited by 17 · see citing works
Fault localization based on wide & deep learning model by mining software behavior
Wang, T., Yu, H., Wang, K., & Su, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.026 · Cited by 17 · see citing works
ChatDBG: Augmenting Debugging with Large Language Models
Levin, K. H., van Kempen, N., Berger, E. D., & Freund, S. N. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729355 · Cited by 16 · see citing works
Fault localization using function call frequencies
Vancsics, B., Horváth, F., Szatmári, A., & Beszédes, Á. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111429 · Cited by 16 · see citing works
Context-based cluster fault localization
Yu, J., Lei, Y., Xie, H., Fu, L., & Liu, C. (2022) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3524610.3527891 · Cited by 16 · see citing works
CounterFault: Value-Based Fault Localization by Modeling and Predicting Counterfactual Outcomes
Podgurski, A., & Kucuk, Y. (2020) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme46990.2020.00044 · Cited by 16 · see citing works
HMBFL: Higher-Order Mutation-Based Fault Localization
Li, Z., Shi, B., Wang, H., Liu, Y., & Chen, X. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00016 · Cited by 16 · see citing works
Influential Global and Local Contexts Guided Trace Representation for Fault Localization
Zhang, Z., Lei, Y., Su, T., Yan, M. L., Mao, X., & Yu, Y. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3576043 · Cited by 16 · see citing works
A pattern-based approach to detect and improve non-descriptive test names
Wu, J., & Clause, J. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2020.110639 · Cited by 16 · see citing works
When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey
Niu, F., Li, C., Liu, K., Xia, X., & Lo, D. (2025) · ACM Computing Surveys · doi:10.1145/3734217 · Cited by 17 · see citing works
How Useful is Code Change Information for Fault Localization in Continuous Integration?
Chen, A. R., Chen, T.-H. P., & Chen, J. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3556931 · Cited by 15 · see citing works
GMBFL: Optimizing Mutation-Based Fault Localization via Graph Representation
Wu, S., Li, Z., Liu, Y., Chen, X., & Li, M. (2023) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58846.2023.00033 · Cited by 15 · see citing works
Locating Faulty Methods with a Mixed RNN and Attention Model
Yang, S., Cao, J., Zeng, H., Shen, B., & Zhong, H. (2021) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc52881.2021.00028 · Cited by 14 · see citing works
Searching for Multi-fault Programs in Defects4J
An, G., Yoon, J., & Yoo, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88106-1_11 · Cited by 14 · see citing works
AGA: An Accelerated Greedy Additional Algorithm for Test Case Prioritization
Li, F., Zhou, J., Li, Y., Hao, D., & Zhang, L. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3137929 · Cited by 14 · see citing works
Self-citationAGFL: A Graph Convolutional Neural Network-Based Method for Fault Localization
Qian, J., Ju, X., Chen, X., Shen, H., & Shen, Y. (2021) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs54544.2021.00077 · Cited by 13 · see citing works
An effective fault localization approach based on PageRank and mutation analysis
Yan, Y. P., Jiang, S., Zhang, Y., & Zhang, C. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111799 · Cited by 13 · see citing works
Self-citationVsusFL: Variable-suspiciousness-based Fault Localization for novice programs
Li, Z., Wu, S., Liu, Y., Shen, J., Wu, Y., Zhang, Z., & Chen, X. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111822 · Cited by 13 · see citing works
CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios
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What You See is What You Get: Attention-Based Self-Guided Automatic Unit Test Generation
Yin, X., Ni, C., Xu, X., & Yang, X. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00105 · Cited by 14 · see citing works
What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness
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MSFL: A Model for Fault Localization Using Mutation-Spectra Technique
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An empirical study of fault localization in Python programs
Rezaalipour, M., & Furia, C. A. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10475-3 · Cited by 12 · see citing works
Wu, Y., Liu, Y., Wang, W., Li, Z., Chen, X., & Doyle, P. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3165126 · Cited by 12 · see citing works
Yan, L., Wen, T., Xie, H., Fu, L., Liu, C., Xu, L., & Sun, H. (2023) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc58990.2023.00045 · Cited by 12 · see citing works
Effective Isolation of Fault-Correlated Variables via Statistical and Mutation Analysis
Wen, M., Xie, Z., Luo, K., Chen, X., Yang, Y., & Jin, H. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3209590 · Cited by 11 · see citing works
A deep semantics-aware data augmentation method for fault localization
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- 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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