Abstract
One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimize project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.