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Machine Learning to Guide Performance Testing: An Autonomous Test Framework
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Research Institutes of Sweden (RISE) SICS, Sweden .
Research Institutes of Sweden (RISE) SICS, Sweden .ORCID iD: 0000-0002-1512-0844
Research Institutes of Sweden (RISE) SICS, Sweden .
Research Institutes of Sweden (RISE) SICS, Sweden .ORCID iD: 0000-0003-1597-6738
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2019 (English)In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019, p. 164-167Conference paper, Published paper (Refereed)
Abstract [en]

Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.

Place, publisher, year, edition, pages
2019. p. 164-167
Keywords [en]
performance requirements, performance testing, test case generation, reinforcement learning, autonomous testing
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-43918DOI: 10.1109/ICSTW.2019.00046ISI: 000477742600022OAI: oai:DiVA.org:mdh-43918DiVA, id: diva2:1325151
Conference
ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 22 Apr 2019, Xi’an, China
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-08-15Bibliographically approved

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Helali Moghadam, MahshidLisper, Björn

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Helali Moghadam, MahshidSaadatmand, MehrdadBohlin, MarkusLisper, Björn
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