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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: 000477742600022Scopus ID: 2-s2.0-85068406208OAI: 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: 2020-04-14Bibliographically approved
In thesis
1. Machine Learning-Assisted Performance Assurance
Open this publication in new window or tab >>Machine Learning-Assisted Performance Assurance
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the growing involvement of software systems in our life, assurance of performance, as an important quality characteristic, rises to prominence for the success of software products. Performance testing, preservation, and improvement all contribute to the realization of performance assurance. Common approaches to tackle challenges in testing, preservation, and improvement of performance mainly involve techniques relying on performance models or using system models or source code. Although modeling provides a deep insight into the system behavior, drawing a well-detailed model is challenging. On the other hand, those artifacts such as models and source code might not be available all the time. These issues are the motivations for using model-free machine learning techniques such as model-free reinforcement learning to address the related challenges in performance assurance.

Reinforcement learning implies that if the optimal policy (way) for achieving the intended objective in a performance assurance process could instead be learnt by the acting system (e.g., the tester system), then the intended objective could be accomplished without advanced performance models. Furthermore, the learnt policy could later be reused in similar situations, which leads to efficiency improvement by saving computation time while reducing the dependency on the models and source code.

In this thesis, our research goal is to develop adaptive and efficient performance assurance techniques meeting the intended objectives without access to models and source code. We propose three model-free learning-based approaches to tackle the challenges; efficient generation of performance test cases, runtime performance (response time) preservation, and performance improvement in terms of makespan (completion time) reduction. We demonstrate the efficiency and adaptivity of our approaches based on experimental evaluations conducted on the research prototype tools, i.e. simulation environments that we developed or tailored for our problems, in different application areas.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 289
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47501 (URN)978-91-7485-463-3 (ISBN)
Presentation
2020-06-02, Online/Zoom, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2020-04-17 Created: 2020-04-14 Last updated: 2020-04-30Bibliographically approved

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Helali Moghadam, MahshidLisper, Björn

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