Learning and statistical model checking of system response times
| Autor: | Robert Korošec, Priska Bauerstätter, Severin Kann, Elisabeth Jöbstl, Rupert Schlick, Richard Schumi, Cristinel Mateis, Bernhard K. Aichernig, Willibald Krenn |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Statistical model checking Test (assessment) Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering Performance prediction Model quality Artificial intelligence Safety Risk Reliability and Quality business computer Software Combined method Test data Statistical hypothesis testing |
| Zdroj: | Software Quality Journal. 27:757-795 |
| ISSN: | 1573-1367 0963-9314 |
| Popis: | Since computers have become increasingly more powerful, users are less willing to accept slow responses of systems. Hence, performance testing is important for interactive systems. However, it is still challenging to test if a system provides acceptable performance or can satisfy certain response-time limits, especially for different usage scenarios. On the one hand, there are performance-testing techniques that require numerous costly tests of the system. On the other hand, model-based performance analysis methods have a doubtful model quality. Hence, we propose a combined method to mitigate these issues. We learn response-time distributions from test data in order to augment existing behavioral models with timing aspects. Then, we perform statistical model checking with the resulting model for a performance prediction. Finally, we test the accuracy of our prediction with hypotheses testing of the real system. Our method is implemented with a property-based testing tool with integrated statistical model checking algorithms. We demonstrate the feasibility of our techniques in an industrial case study with a web-service application. |
| Databáze: | OpenAIRE |
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