The role of Reinforcement Learning in software testing

Context: Software testing is applied to validate the behavior of the software system and identify flaws and bugs. Different machine learning technique types such as supervised and unsupervised learning were utilized in software testing. However, for some complex software testing scenarios, neither supervised nor unsupervised machine learning techniques were adequate. As such, researchers applied Reinforcement Learning (RL) techniques in some cases. However, a systematic overview of the state-of-the-art on the role of reinforcement learning in software testing is lacking.Objective: The objective of this study is to determine how and to what extent RL was used in software testing.Methods: In this study, a Systematic Literature Review (SLR) was conducted on the use of RL in software testing, and 40 primary studies were investigated.Results: This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques.Conclusions: RL has been widely used in software testing but has almost narrowed to two applications. There is a shortage of papers using advanced RL techniques in addition to multi-agent RL. Several challenges were presented in this study.