Papers to be discussed in this session are: , .
 M. Vasic, A. Kanade, P. Maniatis, D. Bieber, and R. singh, “Neural program repair by jointly learning to localize and repair,” in International conference on learning representations, 2019.
 M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” arXiv preprint arXiv:1711.00740, 2017.
 A. Habib and M. Pradel, “Neural bug finding: A study of opportunities and challenges,” arXiv preprint arXiv:1906.00307, 2019.
 V. Murali, S. Chaudhuri, and C. Jermaine, “Finding likely errors with bayesian specifications,” arXiv preprint arXiv:1703.01370, 2017.
 V. Chibotaru, B. Bichsel, V. Raychev, and M. Vechev, “Scalable taint specification inference with big code,” in Proceedings of the 40th acm sigplan conference on programming language design and implementation, 2019, pp. 760–774.
 A. Rice, E. Aftandilian, C. Jaspan, E. Johnston, M. Pradel, and Y. Arroyo-Paredes, “Detecting argument selection defects,” Proceedings of the ACM on Programming Languages, vol. 1, no. OOPSLA, p. 104, 2017.
 T. Kremenek, A. Y. Ng, and D. R. Engler, “A factor graph model for software bug finding.” in IJCAI, 2007, pp. 2510–2516.
 B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu, “On the" naturalness" of buggy code,” in 2016 ieee/acm 38th international conference on software engineering (icse), 2016, pp. 428–439.
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