STUDY OF MODIFIED GENETIC ALGORITHM-SIMULATED ANNEALING FOR THE ESTIMATION OF SOFTWARE EFFORT AND COST

I.Thamarai.1, S. Murugavalli.2 1Research Scholar, Sathyabama University, Chennai, India, 2Supervisor, Sathyabama University, Chennai. Email: 1ilango.thamarai@gmail.com, 2murugavalli26@rediffmail.com Abstract Software Effort Estimation is very crucial estimation task because of the intangible nature of software but it is very essential for developing a software application. Numerous approaches are available in software estimation method such as non-algorithmic models and algorithmic models. The main problems of SE prediction are identification of components of software project and feature selection between the projects. In this paper Modified Genetic Algorithm-Simulated Annealing (MGASA) is proposed to predict the software effort and cost estimation. In MGASA, the estimation of software is based on similar projects. Modified Genetic Algorithm (MGA) is a stochastic search algorithm. The adaptive search process has been effectively utilized in different types of challenging numerical optimization problems. The Simulated Annealing (SA) is an analogy between the way, in which the metal freezes and cools into a minimum energy crystalline structure. The main advantage of using Modified Genetic Algorithm-Simulated Annealing (MGASA) tool is to enhance the accuracy of software effort estimation.