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Exploratory Performance Testing Using Reinforcement Learning

EasyChair Preprint no. 2746

8 pagesDate: February 21, 2020


Performance bottlenecks in software systems resulting in high response times and low throughput 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. This paper presents PerfXRL, a novel approach for finding the combinations of input values which 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 learn to focus on those areas of the input space which reveal performance bottlenecks. The results show that PerfxRL intelligently explores a huge space of combinations of input values and finds 50% of the performance bottlenecks by only exploring the 25% of the input space.

Keyphrases: data generation problem, Deep Neural Network, Deep Reinforcement Learning, machine learning, performance bottleneck, performance testing, Reinforcement Learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Tanwir Ahmad and Adnan Ashraf and Dragos Truscan and Ivan Porres},
  title = {Exploratory Performance Testing Using Reinforcement Learning},
  howpublished = {EasyChair Preprint no. 2746},

  year = {EasyChair, 2020}}
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