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|Publication type:||Conference paper|
|Type of review:||Not specified|
|Title:||DeepTC-Enhancer : improving the readability of automatically generated tests|
|Proceedings:||Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering|
|Conference details:||35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Virtual Event, 21-25 September 2020|
|Publisher / Ed. Institution:||ACM|
|Subjects:||Software testing; Deep learning; Test case generation; Program comprehension; Empirical study|
|Subject (DDC):||006: Special computer methods|
|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). 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 approach, 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 results 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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Information Technology (InIT)|
|Published as part of the ZHAW project:||COSMOS – DevOps for Complex Cyber-physical Systems of Systems|
|Appears in collections:||Publikationen School of Engineering|
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|2021_Roy-etal_DeepTC-Enhancer_ASE.pdf||1.17 MB||Adobe PDF|
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Roy, D., Zhang, Z., Ma, M., Arnaoudova, V., Panichella, A., Panichella, S., Gonzalez, D., & Mirakhorli, M. (2020). DeepTC-Enhancer : improving the readability of automatically generated tests [Conference paper]. Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 287–298. https://doi.org/10.1145/3324884.3416622
Roy, D. et al. (2020) ‘DeepTC-Enhancer : improving the readability of automatically generated tests’, in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. ACM, pp. 287–298. Available at: https://doi.org/10.1145/3324884.3416622.
D. Roy et al., “DeepTC-Enhancer : improving the readability of automatically generated tests,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020, pp. 287–298. doi: 10.1145/3324884.3416622.
Roy, Devjeet, et al. “DeepTC-Enhancer : Improving the Readability of Automatically Generated Tests.” Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, ACM, 2020, pp. 287–98, https://doi.org/10.1145/3324884.3416622.
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