Automated Unit Test Case generation has been the focus of extensive literature within the research community. Existing approaches are usually guided by the test coverage criteria, generating synthetic test cases that are often difficult to read or understand for developers. In this paper we propose AthenaTest, an approach that aims at generating unit test cases by learning from real-world, developer-written test cases. Our approach relies on a state-of-the-art sequence-to-sequence transformer model which is able to write useful test cases for a given method under test (i.e., focal method). We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub. We use this dataset to train a transformer model to translate focal methods into the corresponding test cases. We evaluate the ability of our model in generating test cases using natural language processing as well as code-specific criteria. First, we assess the quality of the translation compared to the target test case, then we analyze properties of the test case such as syntactic correctness and number and variety of testing APIs (e.g., asserts). We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3. Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated test cases.

AthenaTest是一种通过学习从实际的焦点方法和开发人员编写的测试用例生成单元测试用例的方法，使用序列到序列学习任务，利用两步训练过程， 通过自然语言和源代码的预训练以及围绕中心方法的聚焦上下文信息来提高验证损失，并且使用公开可用的最大的有监督平行单元测试用例方法语料库对其进行评估。测试结果表明，AthenaTest方法优于GPT-3，并具有与EvoSuite相当的覆盖率和良好的可读性和理解性。

基于变形金刚和焦点环境的单元测试用例生成