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An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.ORCID iD: 0000-0003-3354-1463
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-1512-0844
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0001-7879-4371
Mälardalen University, Sweden.ORCID iD: 0000-0003-1597-6738
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2022 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, p. 127-159Article in journal (Refereed) Published
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 learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned 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 in a simulated performance testing setup, 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. © 2021, The Author(s).

Place, publisher, year, edition, pages
Springer , 2022. p. 127-159
Keywords [en]
Autonomous testing, Performance testing, Reinforcement learning, Stress testing, Test case generation, Automation, Computer programming languages, Testing, Transfer learning, Automated generation, Optimal performance, Performance Model, Performance testing framework, Performance tests, Simulated performance, Software systems, Software testing
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ri:diva-52628DOI: 10.1007/s11219-020-09532-zScopus ID: 2-s2.0-85102446552OAI: oai:DiVA.org:ri-52628DiVA, id: diva2:1539784
Note

Funding text 1: This work has been supported by and received funding partially from the TESTOMAT, XIVT, IVVES and MegaM@Rt2 European projects.

Available from: 2021-03-25 Created: 2021-03-25 Last updated: 2023-10-04Bibliographically approved

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Helali Moghadam, MahshidSaadatmand, MehrdadBorg, MarkusBohlin, Markus

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