Testing

Introduction

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

Papers

In this session, we will talk about:

• The DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests paper, by Roy et al. [1], where authors make use of a codeseq-like architecture to improve variable and method names of test cases generated through search-based approaches. (We will have Annibale Panichella, one of the authors, as a guest!)

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

• The Unit Test Case Generation with Transformers paper, by Tufano et al. [2] where authors train a transformer model to translate focal methods into the corresponding test cases. (If we have quorum, we can discuss about this one in the lecture too.)

Finally, also not compulsory, but highly recommended:

• A literature review on the opportunities and challenges of neural bug finding, by Habib and Pradel [3].
• The ‘naturalness’ of buggy code, by Ray and colleagues [4]

Bibliography

[1]

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.

[2]

M. Tufano, D. Drain, A. Svyatkovskiy, S. K. Deng, and N. Sundaresan, “Unit test case generation with transformers.” 2020.

[3]

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

[4]

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.

[5]

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

[6]

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.

[7]

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.

[8]

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

Copyright

The course contents are copyrighted (c) 2018,2019,2020 - onwards by TU Delft and their respective authors and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.