Knowledge-enhanced software refinement: leveraging reinforcement learning for search-based quality engineering

Abstract

In the rapidly evolving software development industry, the early identification of optimal design alternatives and accurate performance prediction are critical for developing efficient software products. This paper introduces a novel approach to software refinement, termed Reinforcement Learning-based Software Refinement (RLSR), which leverages Reinforcement Learning techniques to address this challenge. RLSR enables an automated software refinement process that incorporates quality-driven intelligent software development as an early decision-making strategy. By proposing a Q-learning-based approach, RLSR facilitates the automatic refinement of software in dynamic environments while optimizing the utilization of computational resources and time. Additionally, the convergence rate to an optimal policy during the refinement process is investigated. The results demonstrate that training the policy using throughput values leads to significantly faster convergence to optimal rewards. This study evaluates RLSR based on various metrics, including episode length, reward over time, and reward distributions on a running example. Furthermore, to illustrate the effectiveness and applicability of the proposed method, a comparative analysis is applied to three refinable software designs, such as the E-commerce platform, smart booking platform, and Web-based GIS transformation system. The comparison between Q-learning and the proposed algorithm reveals that the refinement outcomes achieved with the proposed algorithm are superior, particularly when an adequate number of learning steps and a comprehensive historical dataset are available. The findings emphasize the potential of leveraging reinforcement learning techniques for automating software refinement and improving the efficiency of the model-driven development process.