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https://doi.org/10.21256/zhaw-22413| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Keine Angabe |
| Titel: | DeepTC-Enhancer : improving the readability of automatically generated tests |
| Autor/-in: | Roy, Devjeet Zhang, Ziyi Ma, Maggie Arnaoudova, Venera Panichella, Annibale Panichella, Sebastiano Gonzalez, Danielle Mirakhorli, Mehdi |
| et. al: | No |
| DOI: | 10.1145/3324884.3416622 10.21256/zhaw-22413 |
| Tagungsband: | Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering |
| Seite(n): | 287 |
| Seiten bis: | 298 |
| Angaben zur Konferenz: | 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Virtual Event, 21-25 September 2020 |
| Erscheinungsdatum: | 2020 |
| Verlag / Hrsg. Institution: | Association for Computing Machinery |
| ISBN: | 978-1-4503-6768-4 |
| Sprache: | Englisch |
| Schlagwörter: | Software testing; Deep learning; Test case generation; Program comprehension; Empirical study |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22413 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | COSMOS – DevOps for Complex Cyber-physical Systems of Systems |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Roy-etal_DeepTC-Enhancer_ASE.pdf | 1.17 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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. Association for Computing Machinery, 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, Ziyi ZHANG, Maggie MA, Venera ARNAOUDOVA, Annibale PANICHELLA, Sebastiano PANICHELLA, Danielle GONZALEZ und Mehdi MIRAKHORLI, 2020. DeepTC-Enhancer : improving the readability of automatically generated tests. In: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. Conference paper. Association for Computing Machinery. 2020. S. 287–298. ISBN 978-1-4503-6768-4
Roy, Devjeet, Ziyi Zhang, Maggie Ma, Venera Arnaoudova, Annibale Panichella, Sebastiano Panichella, Danielle Gonzalez, and Mehdi Mirakhorli. 2020. “DeepTC-Enhancer : Improving the Readability of Automatically Generated Tests.” Conference paper. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 287–98. Association for Computing Machinery. https://doi.org/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, Association for Computing Machinery, 2020, pp. 287–98, https://doi.org/10.1145/3324884.3416622.
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