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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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
ISSN:	0963-9314, 1573-1367
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Abstract	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.
AbstractList	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.
Author	Schumi, Richard Aichernig, Bernhard K. Krenn, Willibald Mateis, Cristinel Kann, Severin Bauerstätter, Priska Jöbstl, Elisabeth Korošec, Robert Schlick, Rupert
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Cites_doi	10.1007/978-3-642-40196-1_30 10.1109/ICST.2009.29 10.1145/2505305.2505310 10.1049/ip-sen:19990151 10.1145/1596550.1596574 10.1007/978-3-642-16612-9_11 10.4135/9781412984522 10.1109/TSE.2006.106 10.1109/FOSE.2007.32 10.4204/EPTCS.85.1 10.1016/j.jss.2008.03.066 10.1109/CSMR.2006.43 10.1145/2633448.2633452 10.1109/TSE.2004.9 10.1145/2034654.2034663 10.1201/9781420010435 10.1002/9781118391686 10.1109/MEMCOD.2016.7797748 10.1109/ICSTW.2016.24 10.1109/ICST.2016.37 10.1109/TASE.2014.20 10.1007/978-3-319-67549-7_18 10.1142/5575 10.1109/RTSS.2012.85 10.1080/01621459.1963.10500830 10.1145/351240.351266 10.1007/978-3-319-99154-2_3 10.1109/MIC.2002.1020328 10.1109/ICST.2017.42
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SubjectTerms	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
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Title	Learning and statistical model checking of system response times
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