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API misuses often lead to software bugs, crashes, and vulnerabilities.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Chen, Z., Kommrusch, S., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., and Monperrus, M · 1959
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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A graph-based approach to api usage adaptation
Nguyen, H. A., Nguyen, T. T., Wilson Jr, G., Nguyen, A. T., Kim, M., and Nguyen, T. N · 2010
Earlier work this paper cites.
Genprog: A generic method for automatic software repair
Le Goues, C., Nguyen, T., Forrest, S., and Weimer, W · 2011
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The most dangerous code in the world: validating ssl certificates in non-browser software
Georgiev, M., Iyengar, S., Jana, S., Anubhai, R., Boneh, D., and Shmatikov, V · 2012
Earlier work this paper cites.
Leveraging test generation and specification mining for automated bug detection without false positives
Pradel, M., and Gross, T. R · 2012
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Statically checking api protocol conformance with mined multi-object specifications
Pradel, M., Jaspan, C., Aldrich, J., and Gross, T. R · 2012
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It’s not a bug, it’s a feature: how misclassification impacts bug prediction
Herzig, K., Just, S., and Zeller, A · 2013
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Detecting missing method calls as violations of the majority rule
Monperrus, M., and Mezini, M · 2013
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Semfix: Program repair via semantic analysis
Nguyen, H. D. T., Qi, D., Roychoudhury, A., and Chandra, S · 2013
Earlier work this paper cites.
Safewapi: Web api misuse detector for web applications
Bae, S., Cho, H., Lim, I., and Ryu, S · 2014
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Defects4j: A database of existing faults to enable controlled testing studies for java programs
Just, R., Jalali, D., and Ernst, M. D · 2014
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The strength of random search on automated program repair
Qi, Y., Mao, X., Lei, Y., Dai, Z., and Wang, C · 2014
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Fixing recurring crash bugs via analyzing q&a sites (t)
Gao, Q., Zhang, H., Wang, J., Xiong, Y., Zhang, L., and Mei, H · 2015
Earlier work this paper cites.
relifix: Automated repair of software regressions
Tan, S. H., and Roychoudhury, A · 2015
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Mubench: A benchmark for api-misuse detectors
Amann, S., Nadi, S., Nguyen, H. A., Nguyen, T. N., and Mezini, M · 2016
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History driven program repair
Le, X. B. D., Lo, D., and Le Goues, C · 2016
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How good are the specs? a study of the bug-finding effectiveness of existing java api specifications
Legunsen, O., Hassan, W. U., Xu, X., Roşu, G., and Marinov, D · 2016
Earlier work this paper cites.
Automatic patch generation by learning correct code
Long, F., and Rinard, M · 2016
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Angelix: Scalable multiline program patch synthesis via symbolic analysis
Mechtaev, S., Yi, J., and Roychoudhury, A · 2016
Earlier work this paper cites.
Nopol: Automatic repair of conditional statement bugs in java programs
Xuan, J., Martinez, M., Demarco, F., Clement, M., Marcote, S. L., Durieux, T., Le Berre, D., and Monperrus, M · 2016
Earlier work this paper cites.
Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge
Lin, D., Koppel, J., Chen, A., and Solar-Lezama, A · 2017
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
A systematic evaluation of static api-misuse detectors
Amann, S., Nguyen, H. A., Nadi, S., Nguyen, T. N., and Mezini, M · 2018
Earlier work this paper cites.
Automated repair of mobile friendly problems in web pages
Mahajan, S., Abolhassani, N., McMinn, P., and Halfond, W. G · 2018
Earlier work this paper cites.
The living review on automated program repair
Monperrus, M · 2018
Earlier work this paper cites.
Bugs. jar: A large-scale, diverse dataset of real-world java bugs
Saha, R. K., Lyu, Y., Lam, W., Yoshida, H., and Prasad, M. R · 2018
Cited alongside, same era.
Repairing crashes in android apps
Tan, S. H., Dong, Z., Gao, X., and Roychoudhury, A · 2018
Cited alongside, same era.
Context-aware patch generation for better automated program repair
Wen, M., Chen, J., Wu, R., Hao, D., and Cheung, S.-C · 2018
Cited alongside, same era.
Identifying patch correctness in test-based program repair
Xiong, Y., Liu, X., Zeng, M., Zhang, L., and Huang, G · 2018
Cited alongside, same era.
Are code examples on an online q&a forum reliable?: a study of api misuse on stack overflow
Zhang, T., Upadhyaya, G., Reinhardt, A., Rajan, H., and Kim, M · 2018
Cited alongside, same era.
Electra: Pre-training text encoders as discriminators rather than generators
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D · 2019
Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
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Unified pre-training for program understanding and generation
Ahmad, W., Chakraborty, S., Ray, B., and Chang, K.-W · 2021
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Tfix: Learning to fix coding errors with a text-to-text transformer
Berabi, B., He, J., Raychev, V., and Vechev, M · 2021
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Elnaggar, A., Ding, W., Jones, L., Gibbs, T., Feher, T., Angerer, C., Severini, S., Matthes, F., and Rost, B · 2021
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Cure: Code-aware neural machine translation for automatic program repair
Jiang, N., Lutellier, T., and Tan, L · 2021
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Cited alongside, same era.
Empirical review of java program repair tools: A large-scale experiment on 2,141 bugs and 23,551 repair attempts
Durieux, T., Madeiral, F., Martinez, M., and Abreu, R · 2019
Cited alongside, same era.
An empirical study on api-misuse bugs in open-source c programs
Gu, Z., Wu, J., Liu, J., Zhou, M., and Gu, M · 2019
Cited alongside, same era.
Using safety properties to generate vulnerability patches
Huang, Z., Lie, D., Tan, G., and Jaeger, T · 2019
Cited alongside, same era.
Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., and Brockschmidt, M · 2019
Cited alongside, same era.
Effective and efficient api misuse detection via exception propagation and search-based testing
Kechagia, M., Devroey, X., Panichella, A., Gousios, G., and van Deursen, A · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C., and Toutanova, L. K · 2019
Cited alongside, same era.
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Active learning of discriminative subgraph patterns for api misuse detection
Kang, H. J., and Lo, D · 2021
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Evaluating automatic program repair capabilities to repair api misuses
Kechagia, M., Mechtaev, S., Sarro, F., and Harman, M · 2021
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A large-scale study on api misuses in the wild
Li, X., Jiang, J., Benton, S., Xiong, Y., and Zhang, L · 2021
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Arbitrar: User-guided api misuse detection
Li, Z., Machiry, A., Chen, B., Naik, M., Wang, K., and Song, L · 2021
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C. B., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., Gong, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S. K., Fu, S., and Liu, S · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Wang, Y., Wang, W., Joty, S. R., and Hoi, S. C. H · 2021
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A syntax-guided edit decoder for neural program repair
Zhu, Q., Sun, Z., Xiao, Y.-a., Zhang, W., Yuan, K., Xiong, Y., and Zhang, L · 2021
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Gao, X., Noller, Y., and Roychoudhury, A · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., and Yin, J · 2022
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Npex: repairing java null pointer exceptions without tests
Lee, J., Hong, S., and Oh, H · 2022
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Dear: A novel deep learning-based approach for automated program repair
Li, Y., Wang, S., and Nguyen, T. N · 2022
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Automating code review activities by large-scale pre-training
Li, Z., Lu, S., Guo, D., Duan, N., Jannu, S., Jenks, G., Majumder, D., Green, J., Svyatkovskiy, A., Fu, S., et al · 2022
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No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence
Wang, C., Yang, Y., Gao, C., Peng, Y., Zhang, H., and Lyu, M. R · 2022
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Less training, more repairing please: revisiting automated program repair via zero-shot learning
Xia, C. S., and Zhang, L · 2022
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A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
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Neural program repair with execution-based backpropagation
Ye, H., Martinez, M., and Monperrus, M · 2022
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Circle: continual repair across programming languages
Yuan, W., Zhang, Q., He, T., Fang, C., Hung, N. Q. V., Hao, X., and Yin, H · 2022
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An extensive study on pre-trained models for program understanding and generation
Zeng, Z., Tan, H., Zhang, H., Li, J., Zhang, Y., and Zhang, L · 2022
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Coditt5: Pretraining for source code and natural language editing
Zhang, J., Panthaplackel, S., Nie, P., Li, J. J., and Gligoric, M · 2022
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Selfapr: Self-supervised program repair with test execution diagnostics
Ye, H., Martinez, M., Luo, X., Zhang, T., and Monperrus, M · 2023
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A survey of learning-based automated program repair
Zhang, Q., Fang, C., Ma, Y., Sun, W., and Chen, Z · 2023
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