Optimal Scheduling of Software Projects Using Reinforcement Learning

We compute optimal scheduling policies for software development projects. We use reinforcement learning as the optimization technique. Our approach is based on a formal, stochastic scheduling model that explicitly captures the strong feedback between the tasks in software development ("ripple effects"). For sample projects, we compute the optimal policy, simulate the project, and analyze the task assignments that are made by the optimal policy. We find that optimal policies typically assign tasks according to the past performance of the developers and the characteristics of the software design. In particular, we address the problem of when to schedule large or strongly coupled components. We also sketch approaches to the optimization of large projects.