Exploratory Performance Testing Using Reinforcement Learning

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Publication Details

List of Authors: Tanwir Ahmad, Adnan Ashraf, Dragos Truscan, Ivan Porres
Publication year: 2019
Publisher: IEEE
Book title: IEEE 45th Euromicro Conference on Software Engineering and Advanced Applications
Start page: 156
End page: 163
ISBN: 978-1-7281-3285-3


Performance bottlenecks resulting in high response times and low throughput of software systems can ruin the reputation of the companies that rely on them. Almost two-thirds of performance bottlenecks are triggered on specific input values. However, finding the input values for performance test cases that can identify performance bottlenecks in a large-scale complex system within a reasonable amount of time is a cumbersome, cost-intensive, and time-consuming task. The reason is that there can be numerous combinations of test input values to explore in a limited amount of time. This paper presents PerfXRL, a novel approach for finding those combinations of input values that can reveal performance bottlenecks in the system under test. Our approach uses reinforcement learning to explore a large input space comprising combinations of input values and to learn to focus on those areas of the input space which trigger performance bottlenecks. The experimental results show that PerfxRL can detect 72% more performance bottlenecks than random testing by only exploring the 25% of the input space.


Artificial neural networks, Performance testing, Reinforcement Learning, Test data generation


Last updated on 2020-30-03 at 08:27