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- Helali Moghadam, MahshidMälardalens högskola,RISE,Industriella system,Mälardalen University, Sweden,Inbyggda system,RISE Research Institutes of Sweden,Software Testing Lab (author)
An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
- Article/chapterEnglish2022
Publisher, publication year, extent ...
- 2021-03-10
- Springer,2022
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:ri-52628
- https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-52628URI
- https://doi.org/10.1007/s11219-020-09532-zDOI
- https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-47471URI
Supplementary language notes
- Language:English
- Summary in:English
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- SwePubSwePub
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Notes
- Funding text 1: This work has been supported by and received funding partially from the TESTOMAT, XIVT, IVVES and MegaM@Rt2 European projects.
- 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).
Subject headings and genre
- TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Datorsystem hsv//swe
- ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems hsv//eng
- 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
- Computer Science
Added entries (persons, corporate bodies, meetings, titles ...)
- Saadatmand, Mehrdad,1980-Mälardalens högskola,RISE,Industriella system,Inbyggda system,RISE Research Institutes of Sweden(Swepub:mdh)msd03 (author)
- Borg, MarkusRISE,Mobilitet och system,RISE Research Institutes of Sweden(Swepub:ri)markus.borg@ri.se (author)
- Bohlin, Markus,1976-Mälardalen University, Sweden,Mälardalens universitet, Innovation och produktrealisering(Swepub:mdh)mbn05 (author)
- Lisper, BjörnMälardalens högskola,Inbyggda system(Swepub:mdh)blr01 (author)
- RISEIndustriella system (creator_code:org_t)
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- In:Software quality journal: Springer, s. 127-1590963-93141573-1367
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