Testing is an expensive activity, and therefore, supporting developers in any software testing related activity is important.


In this session, we will talk about:

While not compulsory, the other very interesting fresh paper that you can read:

Finally, also not compulsory, but highly recommended:


D. Roy et al., “DeepTC-enhancer: Improving the readability of automatically generated tests,” in The 35th IEEE/ACM international conference on automated software engineering, 2020.
M. Tufano, D. Drain, A. Svyatkovskiy, S. K. Deng, and N. Sundaresan, Unit test case generation with transformers.” 2020.
A. Habib and M. Pradel, “Neural bug finding: A study of opportunities and challenges,” arXiv preprint arXiv:1906.00307, 2019.
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.
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.