Large-scale Agile Software Project Scheduling Based on Deep Reinforcement Learning.

This study aimed to solve the scheduling problem of large-scale agile software project. It was decomposed into three strong-coupled subproblems; story selection, story allocation and task allocation. Dynamic events such as the addition and deletion of user stories, the change of employee's working hours in each sprint, and other constraints such as team development speed, task duration and skills were introduced. To maximize the total value of user stories completed by the project, a large-scale agile software project scheduling mathematical model was established. According to the characteristics of the problem, the Markov decision process was designed. Ten state features were used to describe the agile scheduling environment at the beginning of each sprint; 12 composite scheduling rules were designed as candidate actions of the agent; and rewards were defined according to the objective function of the scheduling model. A priority experience replay double deep Q network algorithm based on composite scheduling rules was proposed to solve the built model. The double Q network strategy and priority experience replay strategy were introduced to avoid the over-estimation problem of deep Q network and improve the utilization efficiency of trajectory information in the experience replay pool. In order to verify the effectiveness of the proposed algorithm, experiments were carried out in six large-scale agile software project scheduling numerical examples, and the convergence of the proposed algorithm was analyzed. According to the performance measurement of the algorithm, it was compared with the existing representative algorithm DQN, double deep Q network and 12 single composite scheduling rules. The results showed that it had the highest average cumulative reward value in 6 different numerical examples. [ABSTRACT FROM AUTHOR]