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DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests

Devjeet Roy, Ziyi Zhang, Maggie Ma, Venera Arnaoudova, Annibale Panichella, Sebastiano Panichella, Danielle Gonzalez, Mehdi Mirakhorli

Abstract

Automated test case generation tools have been successfully proposed to reduce the amount of human and infrastructure resources required to write and run test cases. However, recent studies demonstrate that the readability of generated tests is very limited due to (i) uninformative identifiers and (ii) lack of proper documentation. Prior studies proposed techniques to improve test readability by either generating natural language summaries or meaningful methods names. While these approaches are shown to improve test readability, they are also affected by two limitations: (1) generated summaries are often perceived as too verbose and redundant by developers, and (2) readable tests require both proper method names but also meaningful identifiers (within-method readability).

BibTeX
@inproceedings{Roy-al:ASE20,
  author    = {Devjeet Roy and
               Ziyi Zhang and
               Maggie Ma and
               Venera Arnaoudova and
               Annibale Panichella and
               Sebastiano Panichella and
               Danielle Gonzalez and
               Mehdi Mirakhorli},
  title     = {{DeepTC-Enhancer:} Improving the Readability of Automatically Generated Tests},
  booktitle = {ASE},
  pages     = {287--298},
  publisher = {{IEEE}},
  year      = {2020},
}

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