Improving Test Case Generation for RESTful APIs through Seeded Sampling

Bachelor thesis (2020)

Authors

C. de Vries Electrical Engineering, Mathematics and Computer Science

Contributors

A. Panichella Software Engineering - EEMCS (mentor)
Mitchell Olsthoorn Software Engineering - EEMCS (mentor)
Przemysław Pawełczak Embedded Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Chiel de Vries
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Published Date

22-06-2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

To validate the quality of software, test cases are used. These test cases are often manually-written, which is labor-intensive. To avoid this problem, automated software testing was invented. Search-based software testing is a useful tool for developers to automatically generate test cases. However, improvements are still needed to create test cases that compete with manually-written ones.
EvoMaster is a tool that generates system-level test cases for RESTful APIs using the MIO algorithm. An important aspect of this algorithm is sampling new test cases. Currently, EvoMaster employs random and smart sampling to achieve this goal. This paper aims to improve the coverage of the generated tests by expanding the sampling methods with seeded sampling. This method consists of extracting sequences of HTTP requests from manually-written tests and using these to sample new test cases.
Seeded sampling is evaluated on two RESTful APIs with 7 different parameter sets. We show that the addition of seeded sampling can improve the coverage achieved by EvoMaster compared to the current combination of sampling techniques. Nonetheless, this paper has some limitations. It only takes two RESTful APIs into account and has a small amount of benchmark runs to back its findings.