From Learning Agents to Agile Software: Reinforcement Learning's Transformative Role in Requirements Engineering

This paper studies the trans formative role of Reinforcement Learning for Requirements Engineering in the context of software development. The integration of Reinforcement Learning, with its adaptive decision-making capabilities, and Requirements Engineering, focused on systematic requirement analysis, offers a promising interaction to address challenges in dynamic project environments. The paper discusses the potential benefits, including adaptive decision-making, optimization in uncertainty, and intelligent requirement prioritization. However, challenges such as complexity, interpretability, data availability, resource intensiveness, and ethical concerns are identified. The conclusion highlights the trans formative potential of this integration while emphasizing the importance of addressing challenges through interdisciplinary collaboration and responsible adoption in different environments. The paper serves as a broad study of the intersection of Reinforcement Learning and Requirements Engineering, providing insights for practitioners, researchers, and stakeholders in the field of software development.