IMPROVING THE PREDICTION ACCURACY OF SOFTWARE DEVELOPMENT COST MODELS

Software development projects are notorious for being completed behind schedule and over budget and for often failing to meet user requirements. A variety 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 through the application of the recently developed Swarm Intelligence techniques. Recent studies have used data mining techniques to improve the prediction accuracy of COCOMO 81. Our research reconfirms these studies and makes further improvement in the prediction accuracy. The wrapper data mining method is slow and is heavily dependent on the domain experts' heuristics. The Particle Swarm Optimization (POS) meta-heuristic algorithm is fast converging and relies neither on the knowledge of the problem nor on the experts' heuristics, making its application wide and extensive.