Hybrid Genetic-Environmental Adaptation Algorithm to Improve Parameters of COCOMO for Software Cost Estimation

Abstract

The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8% higher than when these algorithms are separately adopted.