Optimizing Software Development Cost Estimates Using Multi-Objective Particle Swarm Optimization
Tad Gonsalves, Kei Yamagishi, Ryo Kawabata, Kiyoshi Itoh · Advances in computational intelligence and robotics book series · 2010
Software development projects are notorious for being completed behind schedule and over budget and for often failing to meet user requirements. A myriad of cost estimation models have been proposed to predict development costs early in the lifecycle with the hope of managing the project well within time and budget. However, studies have reported rather high error rates of prediction even in the case of the well-established and widely acknowledged models. This study focuses on the improvement and fine-tuning of the COCOMO 81 model. Although this model is based on software development practices that were prevalent in the 80s, its wide use in industry and academia, the simple form of the parametric equations and the availability of the data in an online repository make it attractive as a test-bed for further research in software development cost estimation. In this study, we show how the recently developed Computational Intelligence techniques can effectively improve the prediction power of existing models. In particular, we focus on the adaptation of the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in simultaneously minimizing two objective functions – prediction error rate and model complexity. This provides the project manager with an opportunity to choose from a set of optimal solutions represented in a trade-off relationship on the Pareto front. Traditional search algorithms use knowledge of the terrain and expert heuristics to guide the search; such uses make them problem-specific and domain-dependent. The MOPSO meta-heuristic approach relies neither on the knowledge of the problem nor on the experts’ heuristics, making its application wide and extensive.