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Artificial Intelligence Techniques in System Testing

Fulltext:


Authors:

Michael Felderer , Eduard Paul Enoiu, Sahar Tahvili

Research groups:

Software Testing Laboratory
Artificial Intelligence and Intelligent Systems

Publication Type:

Book chapter

Venue:

Optimising the Software Development Process with Artificial Intelligence

Publisher:

Springer

DOI:

https://doi.org/10.1007/978-981-19-9948-2

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.

Bibtex

@incollection{Felderer6733,
author = {Michael Felderer and Eduard Paul Enoiu and Sahar Tahvili},
title = {Artificial Intelligence Techniques in System Testing},
isbn = {978-981-19-9947-5},
editor = {Jos{\'e} Ra{\'u}l Romero, Inmaculada Medina-Bulo, Francisco Chicano},
month = {July},
year = {2023},
booktitle = {Optimising the Software Development Process with Artificial Intelligence},
publisher = {Springer},
url = {http://www.es.mdu.se/publications/6733-}
}