Multiobjective Optimization Using Evolutionary Algorithms in Agile Teams Allocation

Ensuring that the team meets project requirements is essential to ensure the software quality available in the market and the success of the project. In this context, this study evaluated three algorithms for optimizing software engineering problems: NSGAII, SPEA2 and MOCell, in order to support project managers in the composition of agile software development teams. These algorithms were tested in an experiment carried out in a software development company and evaluated in four projects recently executed by the company. The approach considered the characteristics of the project activities, available human resources, human resource profile, project constraints (scope and time for execution) and constraints established by the organization. The algorithms returned solutions with the number of resources needed to carry out the project, as well as resources such as more project qualification, lower cost, and productivity adequate for the term established by the client. The results showed that the three algorithms evaluated presented consistent performances. The NSGAII and SPEA2 had very similar results and behavior, whereas the MOCell presented a better performance in the computational effort and needed a larger population for its saturation.