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Intelligent Load Testing: Self-adaptive Reinforcement Learning-driven Load Runner
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Research Institutes of Sweden.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.
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Load testing with the aim of generating an effective workload to identify performance issues is a time-consuming and complex challenge, particularly for evolving software systems. Current automated approaches mainly rely on analyzing system models and source code, or modeling of the real system usage. However, that information might not be available all the time or obtaining it might require considerable effort. On the other hand, if the optimal policy for generating the proper test workload resulting in meeting the objectives of the testing can be learned by the testing system, testing would be possible without access to system models or source code. We propose a self-adaptive reinforcement learning-driven load testing agent that learns the optimal policy for test workload generation. The agent can reuse the learned policy in subsequent testing activities such as meeting different types of testing targets. It generates an efficient test workload resulting in meeting the objective of the testing adaptively without access to system models or source code. Our experimental evaluation shows that the proposed self-adaptive intelligent load testing can reach the testing objective with lower cost in terms of the workload size, i.e. the number of generated users, compared to a typical load testing process, and results in productivity benefits in terms of higher efficiency.

Keywords [en]
performance testing, load testing, workload generation, reinforcement learning, autonomous testing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-47500OAI: oai:DiVA.org:mdh-47500DiVA, id: diva2:1423032
Available from: 2020-04-13 Created: 2020-04-13 Last updated: 2020-04-17Bibliographically 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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CiteExportLink to record
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Citation style
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