Software planning is very important for the success of a software project. Even if the same developers work on the same project, the time span of the project and the quality of software may change based on the project plan. When software managers plan a software project, they strive to allocate human resources in a more efficient way to produce a better software with less cost. The planning process is, however, time-consuming and complicated, especially when the size of the software project is large. Many approaches have been proposed to help software project managers by providing optimal human resource allocations in terms of minimizing the cost. Previous approaches, however, only concentrated on minimizing the cost, and no existing works have considered the practical issues affecting project schedules in practice. We elicited the practical considerations relating to the human resource allocation problem through discussions with a group of software project experts. The practical considerations can affect the project schedule in practice, but their importance has not been taken into consideration in previous approaches. Reflecting the practical considerations, we propose an approach for solving the human resource allocation problem using a genetic algorithm (GA). We compare our approach to an approach that only considers minimization of the time span. Our evaluation shows that the proposed algorithm considers the practical considerations well, in terms of continuous allocation on relevant tasks, minimization of developer multitasking time, and balance of allocation. We also conducted a survey targeting software developers and managers, and the responses showed that practical considerations are as important as minimizing the cost, and our approach would be helpful to software managers. We also investigate the effect of weight factors and coefficient between sub-scores, and find that it is difficult to consider some practical considerations at the same time.
Associated Control Research for Software Project Schedule and Budget Based on Function Point Method