Knowledge-guided reinforcement learning for self-adaptive software engineering and multi-objective quality optimization

Abstract

The current software engineering field is experiencing growing pressures due to the complexity of software systems, rapidly changing requirements, and high-quality expectations in a dynamic environment. Conventional development and quality assurance strategies are usually inflexible and do not leverage contextual knowledge. This article presents a new paradigm that combines knowledge engineering with reinforcement learning to facilitate intelligent, self-adaptive software engineering processes, thereby addressing a gap in the current literature. The main issue is that there are no unified paradigms that integrate learning-based adaptation with domain knowledge organization to maximize software quality in real time. To present Reinforcement Learning for Self-Adaptive Software Engineering (RL-SEF), a reinforcement learning-based approach that incorporates structured domain knowledge into the RL process, characterized by bidirectional integration between RL and domain knowledge. RL-SEF combines knowledge-guided action filtering, hybrid rule-learning decisions, dynamic multi-objective reward adjustments, and an evolving knowledge base into a single, self-adaptive software engineering framework, in contrast to standalone RL and static knowledge-based approaches. The proposed methodology uses an RL agent with domain-knowledge representations to dynamically observe, investigate, and modify software processes in a feedback-driven manner. In contrast to traditional methods, the framework leverages knowledge-directed exploration and mixed decision-making, which are much more effective and efficient for learning and adaptation. Experimental analysis shows that RL-SEF can make significant contributions to software quality metrics, such as performance, reliability, and maintainability, while minimizing time to adapt and manual intervention. Comparison shows high performance compared to traditional and standalone learning-based models. The most significant contributions in the work are as follows: (i) a knowledge-enhanced reinforcement learning framework of software engineering, (ii) an adaptive quality optimization mechanism through intelligent feedback loops, and (iii) a scalable architecture that applies to the real-life dynamic software environment. The study contributes to the intersection of software engineering and knowledge engineering by facilitating more autonomous, intelligent, and quality-driven development practices.