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1.	Felderer, Michael, 1978-, et al. (author) Artificial Intelligence Techniques in System Testing 2023 In: Optimising the software development process with artificial intelligence. - : Springer Science and Business Media Deutschland GmbH. - 9789811999475 - 9789811999482 ; , s. 221-240 Book chapter (other academic/artistic)abstract System testing is essential for developing high-quality systems, but the degree of automation in system testing is still low. Therefore, there is high potential for Artificial Intelligence (AI) techniques like machine learning, natural language processing, or search-based optimization to improve the effectiveness and efficiency of system testing. This chapter presents where and how AI techniques can be applied to automate and optimize system testing activities. First, we identified different system testing activities (i.e., test planning and analysis, test design, test execution, and test evaluation) and indicated how AI techniques could be applied to automate and optimize these activities. Furthermore, we presented an industrial case study on test case analysis, where AI techniques are applied to encode and group natural language into clusters of similar test cases for cluster-based test optimization. Finally, we discuss the levels of autonomy of AI in system testing.

Felderer, Michael, 1978-, et al. (author)
Artificial Intelligence Techniques in System Testing
2023
In: Optimising the software development process with artificial intelligence. - : Springer Science and Business Media Deutschland GmbH. - 9789811999475 - 9789811999482 ; , s. 221-240
Book chapter (other academic/artistic)abstract
- System testing is essential for developing high-quality systems, but the degree of automation in system testing is still low. Therefore, there is high potential for Artificial Intelligence (AI) techniques like machine learning, natural language processing, or search-based optimization to improve the effectiveness and efficiency of system testing. This chapter presents where and how AI techniques can be applied to automate and optimize system testing activities. First, we identified different system testing activities (i.e., test planning and analysis, test design, test execution, and test evaluation) and indicated how AI techniques could be applied to automate and optimize these activities. Furthermore, we presented an industrial case study on test case analysis, where AI techniques are applied to encode and group natural language into clusters of similar test cases for cluster-based test optimization. Finally, we discuss the levels of autonomy of AI in system testing.

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