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
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Multiple comparisons among means
Dunn, O. J. (1961) · Journal of the American Statistical Association · doi:10.1080/01621459.1961.10482090 · Cited by 5,006
A Tutorial on the Cross-Entropy Method
de Boer, P.-T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005) · Annals of Operations Research · doi:10.1007/s10479-005-5724-z · Cited by 3,110
Halstead, M. H. (1977) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,339
Learning to Rank for Information Retrieval
Liu, T. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000016 · Cited by 1,715
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
Just, R., Jalali, D., & Ernst, M. D. (2014) · Proceedings of the 2014 International Symposium on Software Testing and Analysis · doi:10.1145/2610384.2628055 · Cited by 1,447
Empirical evaluation of the tarantula automatic fault-localization technique
Jones, J. A., & Harrold, M. J. (2005) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/1101908.1101949 · Cited by 1,274
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
A Statistical Approach to Mechanized Encoding and Searching of Literary Information
Luhn, H. P. (1957) · IBM Journal of Research and Development · doi:10.1147/rd.14.0309 · Cited by 1,104
Elements of software science (Operating and programming systems series)
Halstead, M. H. (1977) · Cited by 1,037
On the Accuracy of Spectrum-based Fault Localization
Abreu, R., Zoeteweij, P., & van Gemund, A. V. (2007) · Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION (TAICPART-MUTATION 2007) · doi:10.1109/taic.part.2007.13 · Cited by 939
Gradient methods for minimizing composite objective function
Nesterov, Y. E. (2007) · Cited by 867
Learning Representations by Back-Propagating Errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1888.003.0013 · Cited by 856
Scalable statistical bug isolation
Liblit, B., Naik, M., Zheng, A. X., Aiken, A., & Jordan, M. I. (2005) · ACM SIGPLAN Notices · doi:10.1145/1064978.1065014 · Cited by 740
Automatically learning semantic features for defect prediction
Wang, S., Liu, T., & Tan, L. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884804 · Cited by 720
Multilayer perceptron and neural networks
Popescu, M.-C., Bălaş, V. E., Perescu-Popescu, L., & Mastorakis, N. E. (2009) · doi:10.5555/1639537.1639542 · Cited by 674
An Evaluation of Similarity Coefficients for Software Fault Localization
Abreu, R., Zoeteweij, P., & van Gemund, A. J. C. (2006) · Pacific Rim International Symposium on Dependable Computing · doi:10.1109/prdc.2006.18 · Cited by 604
Are automated debugging techniques actually helping programmers?
Parnin, C., & Orso, A. (2011) · International Symposium on Software Testing and Analysis · doi:10.1145/2001420.2001445 · Cited by 593
A model for spectra-based software diagnosis
Naish, L., Lee, H. J., & Ramamohanarao, K. (2011) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2000791.2000795 · Cited by 553
An empirical analysis of flaky tests
Luo, Q., Hariri, F., Eloussi, L., & Marinov, D. (2014) · SIGSOFT FSE · doi:10.1145/2635868.2635920 · Cited by 551
Defect prediction from static code features: current results, limitations, new approaches
Menzies, T., Milton, Z., Turhan, B., Čukić, B., Jiang, Y., & Bener, A. (2010) · Automated Software Engineering · doi:10.1007/s10515-010-0069-5 · Cited by 470
Evaluating and Improving Fault Localization
Pearson, S. S., Campos, J., Just, R., Fraser, G., Abreu, R., Ernst, M. D., Pang, D., & Keller, B. (2017) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2017.62 · Cited by 460
SemFix: program repair via semantic analysis
Nguyen, H. D. T., Qi, D., Roychoudhury, A., & Chandra, S. (2013) · doi:10.5555/2486788.2486890 · Cited by 423
Improving bug localization using structured information retrieval
Saha, R. K., Lease, M., Sarfraz Khurshid, D., & Perry, D. E. (2013) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2013.6693093 · Cited by 421
Metallaxis‐FL: mutation‐based fault localization
Papadakis, M., & Traon, Y. L. (2015) · Software testing, verification & reliability · doi:10.1002/stvr.1509 · Cited by 384
Zhou, J. H., Zhang, H., & Lo, D. (2012) · doi:10.5555/2337223.2337226 · Cited by 384
Practitioners' expectations on automated fault localization
Kochhar, P. S., Xia, X., Lo, D., & Li, S. (2016) · International Symposium on Software Testing and Analysis · doi:10.1145/2931037.2931051 · Cited by 381
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization
Xie, X., Chen, T. Y., Kuo, F., & Xu, B. (2013) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2522920.2522924 · Cited by 354
Le, X.-B. D., Lo, D., & Goues, C. L. (2016) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2016.76 · Cited by 341
Semantically Enhanced Software Traceability Using Deep Learning Techniques
Guo, J., Cheng, J., & Cleland‐Huang, J. (2017) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2017.9 · Cited by 334
Ask the Mutants: Mutating Faulty Programs for Fault Localization
Moon, S., Kim, Y., Kim, M., & Yoo, S. (2014) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst.2014.28 · Cited by 315
Bug localization using latent Dirichlet allocation
Lukins, S. K., Kraft, N. A., & Etzkorn, L. H. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2010.04.002 · Cited by 314
An Empirical Study of Fault Localization Families and Their Combinations
Zou, D., Liang, J., Xiong, Y., Ernst, M. D., & Zhang, L. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2892102 · Cited by 282
Self-citationChoosing software metrics for defect prediction: an investigation on feature selection techniques
Gao, K., Khoshgoftaar, T. M., Wang, H., & Seliya, N. (2011) · Software Practice and Experience · doi:10.1002/spe.1043 · Cited by 281
FLUCCS: using code and change metrics to improve fault localization
Sohn, J., & Yoo, S. (2017) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3092703.3092717 · Cited by 252
ASTOR: a program repair library for Java (demo)
Martínez, M., & Monperrus, M. (2016) · International Symposium on Software Testing and Analysis · doi:10.1145/2931037.2948705 · Cited by 240
Mutation analysis of program test data
Budd, T. A. (1980) · Cited by 236
Learning to Combine Multiple Ranking Metrics for Fault Localization
Xuan, J., & Monperrus, M. (2014) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2014.41 · Cited by 224
Effective Fault Localization using Code Coverage
Wong, W. E., Qi, Y., Zhao, L., & Cai, K. (2007) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2007.109 · Cited by 210
A learning-to-rank based fault localization approach using likely invariants
Le, T.-D. B., Lo, D., Goues, C. L., & Grunske, L. (2016) · International Symposium on Software Testing and Analysis · doi:10.1145/2931037.2931049 · Cited by 206
BP NEURAL NETWORK-BASED EFFECTIVE FAULT LOCALIZATION
Wong, W. E., & Qi, Y. (2009) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s021819400900426x · Cited by 200
Repairing Programs with Semantic Code Search (T)
Ke, Y., Stolee, K. T., Goues, C. L., & Brun, Y. (2015) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.60 · Cited by 196
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-citationTransforming programs and tests in tandem for fault localization
Li, X., & Zhang, L. (2017) · Proceedings of the ACM on Programming Languages · doi:10.1145/3133916 · Cited by 162
Self-citationEvaluating the usefulness of IR-based fault localization techniques
Wang, Q., Parnin, C., & Orso, A. (2015) · International Symposium on Software Testing and Analysis · doi:10.1145/2771783.2771797 · Cited by 155
Experimental evaluation of using dynamic slices for fault location
Zhang, X., He, H., Gupta, N., & Gupta, R. (2005) · International Symposium on Automated Analysis-Driven Debugging (AADEBUG) · doi:10.1145/1085130.1085135 · Cited by 152
Ranking Measures and Loss Functions in Learning to Rank
Chen, W., Liu, T., Lan, Y., Ma, Z.-M., & Li, H. (2009) · Cited by 151
Boosting spectrum-based fault localization using PageRank
Zhang, M., Li, X., Zhang, L., & Sarfraz Khurshid, D. (2017) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3092703.3092731 · Cited by 148
Self-citationInformation retrieval and spectrum based bug localization: better together
Le, T.-D. B., Oentaryo, R. J., & Lo, D. (2015) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2786805.2786880 · Cited by 146
Codeflaws: a programming competition benchmark for evaluating automated program repair tools
Tan, S. H., Yi, J., Yulis, Mechtaev, S., & Roychoudhury, A. (2017) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-c.2017.76 · Cited by 136
Contract-based program repair without the contracts
Chen, L., Pei, Y., & Furia, C. A. (2017) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2017.8115674 · Cited by 119
Test generation via Dynamic Symbolic Execution for mutation testing
Zhang, L., Xie, T., Zhang, L., Tillmann, N., de Halleux, J., & Mei, H. (2010) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2010.5609672 · Cited by 117
Self-citationA feasibility study of using automated program repair for introductory programming assignments
Yi, J., Ahmed, U. Z., Karkare, A., Tan, S. H., & Roychoudhury, A. (2017) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3106237.3106262 · Cited by 113
Software Fault Localization Using DStar (D*)
Wong, W. E., Debroy, V., Li, Y., & Gao, R. (2012) · doi:10.1109/sere.2012.12 · Cited by 110
Localizing failure-inducing program edits based on spectrum information
Zhang, L., Kim, M., & Sarfraz Khurshid, D. (2011) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2011.6080769 · Cited by 110
Self-citationUsing Mutants to Locate "Unknown" Faults
Papadakis, M., & Traon, Y. L. (2012) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst.2012.159 · Cited by 110
Gray, D. H., Bowes, D., Davey, N., Sun, Y., & Christianson, B. (2009) · Communications in computer and information science · doi:10.1007/978-3-642-03969-0_21 · Cited by 105
Extended comprehensive study of association measures for fault localization
Lucia, L., Lo, D., Jiang, L., Thung, F., & Budi, A. (2013) · Journal of Software Evolution and Process · doi:10.1002/smr.1616 · Cited by 103
Using Machine Learning to Support Debugging with Tarantula
Briand, L., Labiche, Y., & Liu, X. (2007) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre.2007.31 · Cited by 99
Fault Localization Analysis Based on Deep Neural Network
Zheng, W., Hu, D., & Wang, J. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/1820454 · Cited by 96
Provably Optimal and Human-Competitive Results in SBSE for Spectrum Based Fault Localisation
Xie, X., Kuo, F., Chen, T. Y., Yoo, S., & Harman, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39742-4_17 · Cited by 91
Semantic program repair using a reference implementation
Mechtaev, S., Nguyen, M.-D., Noller, Y., Grunske, L., & Roychoudhury, A. (2018) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3180155.3180247 · Cited by 85
Visualization for Fault Localization
Jones, J. A., Harrold, M. J., & Stasko, J. (2003) · Cited by 83
Keller, F., Grunske, L., Heiden, S., Filieri, A., van Hoorn, A., & Lo, D. (2017) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs.2017.22 · Cited by 83
Injecting mechanical faults to localize developer faults for evolving software
Zhang, L., Zhang, L., & Khurshid, S. (2013) · doi:10.1145/2509136.2509551 · Cited by 72
Self-citationEffective fault localization via mutation analysis
Papadakis, M., & Traon, Y. L. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2554978 · Cited by 62
Deep Learning-Based Fault Localization with Contextual Information
Zhang, Z., Lei, Y., Tan, Q., Mao, X., Ping, Z. E. N. G., & CHANG, X. (2017) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edl8143 · Cited by 53
Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset
Martinez, M., Durieux, T., Xuan, J., Sommerard, R., & Monperrus, M. (2015) · arXiv · doi:10.48550/arxiv.1505.07002 · Cited by 47
How Does Execution Information Help with Information-Retrieval Based Bug Localization?
Dao, T. M., Zhang, L., & Meng, N. (2017) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2017.29 · Cited by 37
Self-citationIterative User-Driven Fault Localization
Li, X., d’Amorim, M., & Orso, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49052-6_6 · Cited by 31
Self-citationData-guided repair of selection statements
Gopinath, D., Sarfraz Khurshid, D., Saha, D., & Chandra, S. (2014) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2568225.2568303 · Cited by 29
A large-scale study of call graph-based impact prediction using mutation testing
Musco, V., Monperrus, M., & Preux, P. (2016) · Software Quality Journal · doi:10.1007/s11219-016-9332-8 · Cited by 24
A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing
Musco, V. (., Monperrus, M., & Preux, P. (. (2017) · LillOA (Université de Lille (University Of Lille)) · Cited by 11
Locating faulty code by multiple points slicing
ZhangXiangyu, GuptaNeelam, & GuptaRajiv (2007) · doi:10.5555/1276842.1276844 · Cited by 4
Flagged first, then by citations
3 of the 87 references couldn’t be matched to a known paper.
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
