一句话：提出 DeepTC-enhancer，旨在同时提升自动生成测试的方法名与方法内标识符可读性，并缓解摘要冗长问题。
自动测试用例生成可减少人工编写与运行测试的资源消耗，但已有研究发现生成测试可读性有限，主要因为标识符缺乏信息量且缺少恰当文档。已有工作通过生成自然语言摘要或有意义的方法名来提升可读性，但仍存在两点不足：生成的摘要常被开发者认为过于冗长和冗余；可读测试不仅需要合适的方法名，还需要方法内部标识符具有意义。本文提出 DeepTC-enhancer，针对上述问题探索自动生成测试的可读性增强。所给摘要未给出具体实现与评估细节。
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).
@inproceedings{paperbot5882,
title = {DeepTC-enhancer},
author = {Devjeet Roy and Ziyi Zhang and Maggie Ma and Venera Arnaoudova and Annibale Panichella and Sebastiano Panichella and Danielle Gonzalez and Mehdi Mirakhorli},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3416622}
}