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Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general.
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Smith, E.K., Barr, E.T., Goues, C.L., Brun, Y.: Is the cure worse than the disease? overfitting in automated program repair. In: Foundations of Software Engineering (ESEC/FSE) (2015)
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Long, F., Rinard, M.: Automatic patch generation by learning correct code. In: ACM SIGPLAN Notices (2016)
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Pu, Y., Narasimhan, K., Solar-Lezama, A., Barzilay, R.: sk_p: a neural program corrector for moocs. In: ACM SIGPLAN (2016)
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Gupta, R.R., Pal, S., Kanade, A., Shevade, S.K.: Deepfix: Fixing common c language errors by deep learning. In: AAAI (2017)
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Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017)
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Wang, L., Schwing, A., Lazebnik, S.: Diverse and accurate image description using a variational auto-encoder with an additive gaussian encoding space. In: NIPS (2017)
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Deshpande, A., Aneja, J., Wang, L., Schwing, A.G., Forsyth, D.: Fast, diverse and accurate image captioning guided by part-of-speech. In: CVPR (2019)
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Closest in time.
Goues, C.L., Pradel, M., Roychoudhury, A.: Automated program repair. Commun. ACM 62
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Gupta, R., Kanade, A., Shevade, S.: Deep reinforcement learning for programming language correction. In: AAAI (2019)
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Li, Y., Wang, S., Nguyen, T.N.: Dlfix: Context-based code transformation learning for automated program repair. In: International Conference on Software Engineering (ICSE) (2020)
2020
Closest in time.
Yasunaga, M., Liang, P.: Graph-based, self-supervised program repair from diagnostic feedback. In: ICML (2020)
2020
Closest in time.