Reinforcement Learning in the Software Development Lifecycle: Methods, Applications, and Reward Design

Source abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for addressing sequential decision-making challenges across the software development life cycle (SDLC). To understand how this paradigm is being operationalized in practice, this systematic literature review examines 85 studies published between January 2020 and March 2026 that apply RL to the seven core SDLC phases: planning, analysis, design, development, testing, deployment, and maintenance. We analyze publication trends and venues, map RL applications to specific SDLC tasks, catalog the RL algorithms employed, and provide a taxonomy of reward modeling strategies and their parameters.Our findings reveal a rapid growth of RL-for-SDLC research since 2022, with the testing phase being the most extensively studied (56.47% of studies) while other phases show steady expansion. Reward designs have evolved from sparse binary outcomes (e.g., test-pass/fail) to dense, multi-objective signals that incorporate execution semantics, coverage metrics, intrinsic curiosity, formal verification feedback, and human preferences. We highlight common reward patterns, their influence on learning behavior, and trade-offs that frequently lead to reward hacking or unstable training in software environments. This survey provides researchers and practitioners with a structured overview of how RL is operationalized across the SDLC, emphasizing reward engineering as a critical factor in agent effectiveness. We conclude by outlining promising directions for future research, including broader lifecycle coverage and the development of more robust reward mechanisms for production-ready software engineering automation.