An Artificial Immune System for Black Box Test Case Selection
| Autor: | Jörg Hähner, Lukas Rosenbauer, Anthony Stein |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Artificial immune system business.industry White-box testing 02 engineering and technology Machine learning computer.software_genre Black-box testing Set (abstract data type) Test case Transformation (function) 020204 information systems New product development 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence ddc:004 Bio-inspired computing business computer |
| Zdroj: | Evolutionary Computation in Combinatorial Optimization ISBN: 9783030729035 EvoCOP |
| DOI: | 10.1007/978-3-030-72904-2_11 |
| Popis: | Testing is a crucial part of the development of a new product. For software validation a transformation from manual to automated tests can be observed which enables companies to implement large numbers of test cases. However, during testing situations may occur where it is not feasible to run all tests due to time constraints. Hence a set of critical test cases must be compiled which usually fulfills several criteria. Within this work we focus on criteria that are feasible for black box testing such as system tests. We adapt an existing artificial immune system for our use case and evaluate our method in a series of experiments using industrial datasets. We compare our approach with several other test selection methods where our algorithm shows superior performance. |
| Databáze: | OpenAIRE |
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