Writing tests is a time-consuming yet essential task during software development. We propose to leverage recent advances in deep learning for text and code generation to assist developers in writing tests. We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test. We develop TeCo -- a deep learning model using code semantics for test completion. The key insight underlying TeCo is that predicting the next statement in a test method requires reasoning about code execution, which is hard to do with only syntax-level data that existing code completion models use. TeCo extracts and uses six kinds of code semantics data, including the execution result of prior statements and the execution context of the test method. To provide a testbed for this new task, as well as to evaluate TeCo, we collect a corpus of 130,934 test methods from 1,270 open-source Java projects. Our results show that TeCo achieves an exact-match accuracy of 18, which is 29% higher than the best baseline using syntax-level data only. When measuring functional correctness of generated next statement, TeCo can generate runnable code in 29% of the cases compared to 18% obtained by the best baseline. Moreover, TeCo is significantly better than prior work on test oracle generation.

翻译：编写测试是软件开发中耗时但必要的任务。我们提出利用深度学习和文本与代码生成领域的最新进展来辅助开发者编写测试。我们形式化定义了测试补全这一新任务，即根据测试方法中先前语句的上下文和被测代码，自动补全测试方法中的下一条语句。我们开发了TeCo——一个利用代码语义进行测试补全的深度学习模型。TeCo的核心洞见在于：预测测试方法中的下一条语句需要推理代码执行过程，而现有代码补全模型仅使用语法级数据难以实现。TeCo提取并使用了六种代码语义数据，包括先前语句的执行结果和测试方法的执行上下文。为了给这一新任务提供测试平台并评估TeCo，我们收集了来自1,270个开源Java项目的130,934个测试方法组成的语料库。结果表明，TeCo的精确匹配准确率达18%，比仅使用语法级数据的最佳基线高29%。在度量生成下一条语句的功能正确性时，TeCo在29%的案例中能生成可运行代码，而最佳基线为18%。此外，TeCo在测试预言生成任务上也显著优于先前工作。