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An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Research Institutes of Sweden. (Software Testing Lab)ORCID iD: 0000-0003-3354-1463
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Research Institutes of Sweden.ORCID iD: 0000-0002-1512-0844
RISE Research Institutes of Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-1597-6738
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(English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367Article in journal (Refereed) Submitted
Abstract [en]

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learnt by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learnt policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments on a simulated environment, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process, and performs adaptively without access to source code and performance models.

Place, publisher, year, edition, pages
Springer.
Keywords [en]
Performance testing, Stress testing, Test case generation, Reinforcement learning, Autonomous testing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-47471OAI: oai:DiVA.org:mdh-47471DiVA, id: diva2:1421895
Available from: 2020-04-06 Created: 2020-04-06 Last updated: 2020-05-08Bibliographically 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, MahshidSaadatmand, MehrdadBohlin, MarkusLisper, Björn

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