A model for work distribution in global software development based on machine learning techniques

Global Software Development (GSD) initiative aims to facilitate software development process by providing access to skilled workers at a relatively low cost and 24/7 software development model. Previous work suggests that half of the companies that have tried GSD have failed to realize the anticipated outcomes which have resulted in poor outsourcing relationships, high costs and overall poor software products. One critical factor for successful GSD projects is the allocation of tasks as project managers not only need to consider their workforce but also need to take into the account the characteristics of the geographically distributed sites and their relationships. In this paper, we present a task allocation model based on neural network that identifies a fit site for a given task and then finds related sites to the fit site. The related sites can be used as alternatives to the fit site or as additional sites to run more tasks in parallel. The proposed model provides project managers with a list of potential sites for the given tasks to select the appropriate GSD sites. We also discuss and evaluate the proposed task allocation model compared with other approaches.