Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
| Autor: | Barış Aktemur, Ceren Sahin Gebizli, Hasan Sözer, M. Furkan Kıraç |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry White-box testing Functional testing Random testing Robustness testing 020207 software engineering Manual testing 02 engineering and technology Machine learning computer.software_genre Test case 020204 information systems Fault coverage 0202 electrical engineering electronic engineering information engineering Exploratory testing Artificial intelligence Safety Risk Reliability and Quality business computer Software |
| Zdroj: | Software Quality Journal. 27:861-883 |
| ISSN: | 1573-1367 0963-9314 |
| DOI: | 10.1007/s11219-018-9439-1 |
| Popis: | We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model together with manually-applied test cases for robustness testing. We use this combination to train a long short-term memory (LSTM) network. As a result, the network learns an implicit representation of the usage behavior that is liable to failures. We use this network to generate new event sequences as test cases. We applied our approach in the context of an industrial case study for the black-box testing of a digital TV system. LSTM-generated test cases were able to reveal several faults, including critical ones, that were not detected with existing automated or manual testing activities. Our approach is complementary to model-based and exploratory testing, and the combined approach outperforms random testing in terms of both fault coverage and execution time. |
| Databáze: | OpenAIRE |
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