Search based software project management

This thesis investigates the application of Search Based Software Engineering (SBSE) approach in the field of Software Project Management (SPM). With SBSE approaches, a pool of candidate solutions to an SPM problem is automatically generated and gradually evolved to be increasingly more desirable. The thesis is motivated by the observation from industrial practice that it is much more helpful to the project manager to provide insightful knowledge than exact solutions. We investigate whether SBSE approaches can aid the project managers in decision making by not only providing them with desirable solutions, but also illustrating insightful “what-if” scenarios during the phases of project initiation, planning and enactment. SBSE techniques can automatically “evolve” solutions to software requirement elicitation, project staffing and scheduling problems. However, the current state-ofthe-art computer-aided software project management tools remain limited in several aspects. First, software requirement engineering is plagued by problems associated with unreliable estimates. The estimations made early are assumed to be accurate, but the projects are estimated and executed in an environment filled with uncertainties that may lead to delay or disruptions. Second, software project scheduling and staffing are two closely related problems that have been studied separately by most published research in the field of computer aided software project management, but software project managers are usually confronted with the complex trade-off and correlations of scheduling and staffing. Last, full attendance of required staff is usually assumed after the staff have been assigned to the project, but the execution of a project is subject to staff absences because of sickness and turnover, for example. This thesis makes the following main contributions: (1) Introducing an automated SBSE approach to Sensitivity Analysis for requirement elicitation, which helps to achieve more accurate estimations by directing extra estimation effort towards those error-sensitive requirements and budgets. (2) Demonstrating that Coevolutionary approaches can simultaneously co-evolve solutions for both work package sequencing and project team sizing. The proposed approach to these two interrelated problems yields better results than random and single-population evolutionary algorithms. (3) Presenting co-evolutionary approaches that can guide the project manager to anticipate and ameliorate the impact of staff absence. (4) The investigations of seven sets of real world data on software requirement and software project plans reveal general insights as well as exceptions of our approach in practise. (5) The establishment of a tool that implements the above concepts. These contributions support the thesis that automated SBSE tools can be beneficial to solution generation, and most importantly, insightful knowledge for decision making in the practise of software project management.