Papers to be discussed in this session are: [1], [2].

Bibliography

[1] 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.

[2] M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” arXiv preprint arXiv:1711.00740, 2017.

[3] A. Habib and M. Pradel, “Neural bug finding: A study of opportunities and challenges,” arXiv preprint arXiv:1906.00307, 2019.

[4] V. Murali, S. Chaudhuri, and C. Jermaine, “Finding likely errors with bayesian specifications,” arXiv preprint arXiv:1703.01370, 2017.

[5] 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.

[6] 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.

[7] T. Kremenek, A. Y. Ng, and D. R. Engler, “A factor graph model for software bug finding.” in IJCAI, 2007, pp. 2510–2516.

[8] 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.