The adjusted analogy-based software effort estimation based on similarity distances

Abstract

Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.