Learning and statistical model checking of system response times

Learning and statistical model checking of system response times

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-ti...

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Bibliographic Details
Published in:Software quality journal Vol. 27; no. 2; pp. 757 - 795
Main Authors: Aichernig, Bernhard K., Bauerstätter, Priska, Jöbstl, Elisabeth, Kann, Severin, Korošec, Robert, Krenn, Willibald, Mateis, Cristinel, Schlick, Rupert, Schumi, Richard
Format: Journal Article
Language:English
Published: New York Springer US 01-06-2019
Springer Nature B.V
Subjects:
Compilers
Computer Science
Computer simulation
Data Structures and Information Theory
Feasibility studies
Interactive systems
Interpreters
Monte Carlo simulation
Operating Systems
Performance prediction
Probability
Programming Languages
Response time
Software Engineering/Programming and Operating Systems
Software quality
Statistical models
Stochastic models
User behavior
User profiles
Web services
Workloads
User profiles
Model-based testing
Response time
Cost learning
Statistical model checking
Performance testing
FsCheck
Property-based testing
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Summary: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.
ISSN:0963-9314
1573-1367
DOI:10.1007/s11219-018-9432-8