Automated test case generation tools have been successfully pro- posed 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). In this work, we combine template based methods and Deep Learning (DL) approaches to automatically generate test case scenarios (elicited from natural language patterns of test case statements) as well as to train DL models on path-based representations of source code to generate meaningful identifier names. Our ap- proach, called DeepTC-Enhancer , recommends documentation and identifier names with the ultimate goal of enhancing readability of automatically generated test cases. An empirical evaluation with 36 external and internal developers shows that (1) DeepTC-Enhancer outperforms significantly the baseline approach for generating summaries and performs equally with the baseline approach for test case renaming, (2) the transformation proposed by DeepTC-Enhancer result in a significant increase in readability of automatically generated test cases, and (3) there is a significant difference in the feature preferences between external and internal developers.
Devjeet Roy, Ziyi Zhang, Maggie Ma, Venera Arnaoudova, Annibale Panichella, Sebastiano Panichella, Danielle Gonzalez, Mehdi Mirakhorli