Juan Carlos Alonso Valenzuela - Academia.edu (original) (raw)
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University of the Basque Country, Euskal Herriko Unibertsitatea
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Papers by Juan Carlos Alonso Valenzuela
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021
Testing web APIs automatically requires generating input data values such as addressess, coordina... more Testing web APIs automatically requires generating input data values such as addressess, coordinates or country codes. Generating meaningful values for these types of parameters randomly is rarely feasible, which means a major obstacle for current test case generation approaches. In this paper, we present ARTE, the first semantic-based approach for the Automated generation of Realistic TEst inputs for web APIs. Specifically, ARTE leverages the specification of the API under test to search for meaningful test inputs for the API parameters in knowledge bases like DBpedia. Our approach has been integrated into RESTest, a state-of-the-art tool for API testing, achieving an unprecedented level of automation which allows to generate up to 100% more valid API calls than existing fuzzing techniques, 30% on average. Evaluation results on a set of 26 real-world APIs show that ARTE can generate realistic inputs for 7 out of every 10 parameters, outperforming related approaches. CCS CONCEPTS • Software and its engineering → Software testing and debugging; • Information systems → RESTful web services.
IEEE Transactions on Software Engineering, 2022
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021
Testing web APIs automatically requires generating input data values such as addressess, coordina... more Testing web APIs automatically requires generating input data values such as addressess, coordinates or country codes. Generating meaningful values for these types of parameters randomly is rarely feasible, which means a major obstacle for current test case generation approaches. In this paper, we present ARTE, the first semantic-based approach for the Automated generation of Realistic TEst inputs for web APIs. Specifically, ARTE leverages the specification of the API under test to search for meaningful test inputs for the API parameters in knowledge bases like DBpedia. Our approach has been integrated into RESTest, a state-of-the-art tool for API testing, achieving an unprecedented level of automation which allows to generate up to 100% more valid API calls than existing fuzzing techniques, 30% on average. Evaluation results on a set of 26 real-world APIs show that ARTE can generate realistic inputs for 7 out of every 10 parameters, outperforming related approaches. CCS CONCEPTS • Software and its engineering → Software testing and debugging; • Information systems → RESTful web services.
IEEE Transactions on Software Engineering, 2022