A Multi-agent Reinforcement Learning Risk Management Model for Distributed Agile Software Projects

Nowadays, due to the benefits of the agile method, such as changing rapidly and fast delivery of working software, most software projects are naturally distributed agile projects (DAD). DAD team members work from various remote sites. This leads to the emergence of many significant challenges in risk management. Hence, it requires risk safety techniques to be implemented to mitigate the occurrence of risks. There is no standardized process for teams to deal with DAD risks. In this paper, a multi-agent reinforcement learning risk management model for distributed agile software projects is proposed. The proposed model is implemented to apply a dynamic policy. The model is applied as an experiment to definite numbers for a set of risk factors such as communication and coordination risk, project management risk, and SDLC risk. The proposed model is developed using multiple individual learning using the Q- learning algorithm. A DAD project with two projects is used to evaluate the proposed model. The proposed model was assessed and analyzed for its effectiveness. The results of the evaluation are given.