The public defense of Mahshid Helali Moghadam's Licentiate thesis in Computer Science and Engineering
The public defense of Mahshid Helali Moghadam's Licentiate thesis in Computer Science and Engineering will take place at Mälardalen University room Delta and digital at 09.15 on June 02, 2020.
Title: "Machine Learning-Assisted Performance Assurance"
Serial number: 289
The faculty examiner is Research Professor Shaukat Ali, Simula, Norge, and the examining committee consists of Professor Benoit Baudry, KTH; Assistant Professor Gregory Gay, Chalmers; Research Professor Shaukat Ali, Simula, Norge.
Reserve: Adjunct Professor Johan Åkerberg, Mälardalen University.
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 of 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 which we developed or tailored for our problems, in different application areas.