Syntest-JavaScript: Automated Unit-Level Test Case Generation for JavaScript
Pages 21 - 24
Abstract
Over the last decades, various tools (e.g., AUSTIN and EvoSuite) have been developed to automate the process of unit-level test case generation. Most of these tools are designed for statically-typed languages, such as C and Java. However, as is shown in recent Stack Overflow developer surveys, the popularity of dynamically-typed languages, such as JavaScript and Python, has been increasing and is dominating the charts. Only recently, tools for automated test case generation of dynamically-typed languages have started to emerge (e.g., Pynguin for Python). However, to the best of our knowledge, there is no tool that focuses on automated test case generation for server-side JavaScript. To this aim, we introduce SynTest-JavaScript, a user-friendly tool for automated unit-level test case generation for (server-side) JavaScript. To showcase the effectiveness of SynTest-JavaScript, we empirically evaluate it on five large open-source JavaScript projects and one artificial one.
References
[1]
Shay Artzi, Julian Dolby, Simon Holm Jensen, Anders Møller, and Frank Tip. 2011. A framework for automated testing of JavaScript web applications. In Proceedings of the 33rd International Conference on Software Engineering. 571--580.
[2]
José Campos, Yan Ge, Nasser Albunian, Gordon Fraser, Marcelo Eler, and Andrea Arcuri. 2018. An empirical evaluation of evolutionary algorithms for unit test suite generation. Information and Software Technology 104 (2018), 207--235.
[3]
William Jay Conover. 1998. Practical nonparametric statistics. Vol. 350. John Wiley & Sons.
[4]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197.
[5]
Guodong Li, Esben Andreasen, and Indradeep Ghosh. 2014. SymJS: automatic symbolic testing of JavaScript web applications. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. 449--459.
[6]
Stephan Lukasczyk and Gordon Fraser. 2022. Pynguin: Automated Unit Test Generation for Python. arXiv preprint arXiv:2202.05218 (2022).
[7]
Phil McMinn. 2004. Search-based software test data generation: a survey. Software testing, Verification and reliability 14, 2 (2004), 105--156.
[8]
Shabnam Mirshokraie, Ali Mesbah, and Karthik Pattabiraman. 2013. PYTHIA: Generating test cases with oracles for JavaScript applications. In 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 610--615.
[9]
Shabnam Mirshokraie, Ali Mesbah, and Karthik Pattabiraman. 2015. JSeft: Automated JavaScript unit test generation. In 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST). 1--10.
[10]
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2015. Reformulating branch coverage as a many-objective optimization problem. In 2015 IEEE 8th international conference on software testing, verification and validation (ICST). 1--10.
[11]
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017. Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Transactions on Software Engineering 44, 2 (2017), 122--158.
[12]
Dimitri Stallenberg, Mitchell Olsthoorn, and Annibale Panichella. 2022. Guess What: Test Case Generation for Javascript with Unsupervised Probabilistic Type Inference. In International Symposium on Search Based Software Engineering. Springer, 67--82.
[13]
András Vargha and Harold D Delaney. 2000. A critique and improvement of the CL common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics 25, 2 (2000), 101--132.
Index Terms
- Syntest-JavaScript: Automated Unit-Level Test Case Generation for JavaScript
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Information
Published In
April 2024
84 pages
ISBN:9798400705625
DOI:10.1145/3643659
This work is licensed under a Creative Commons Attribution International 4.0 License.
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- Faculty of Engineering of University of Porto
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Association for Computing Machinery
New York, NY, United States
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Published: 10 September 2024
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