Estimation is an important part of software engineering projects, and the ability to produce accurate effort estimates has an impact on key economic processes, including budgeting and bid proposals and deciding the execution boundaries of the project. Work in this paper explores the interrelationship among different dimensions of software projects, namely, project size, effort, and effort influencing factors. The study aims at providing better effort estimate on the parameters of modified COCOMO along with the detailed use of binary genetic algorithm as a novel optimization algorithm. Significance of 15 cost drivers can be shown by their impact on MMRE of efforts on original 63 NASA datasets. Proposed method is producing tuned values of the cost drivers, which are effective enough to improve the productivity of the projects. Prediction at different levels of MRE for each project reflects the percentage of projects with desired accuracy. Furthermore, this model is validated on two different datasets which represents better estimation accuracy as compared to the COCOMO 81 based NASA 63 and NASA 93 datasets. 1. Introduction Estimation is an important part of software engineering projects, and the ability to produce accurate effort estimates has an impact on key economic processes, including budgeting and bid proposals and deciding the execution boundaries of the project [1]. Effort estimation is a critical activity for planning and monitoring software project development and for delivering the product on time and within budget. Also, feasibility of project in terms of cost and ability to meet customer’s requirements is considered in the process of estimation [2]. The prediction of the effort to be consumed in a software project is, probably, the most sought after variable in the process of project management. The determination of the value of this variable in the early stages of a software project drives the planning of remaining activities. The estimation activity is plagued with uncertainties and obstacles, and the measurement of past projects is a necessary step for solving the question. The problem of accurate effort estimation is still open and the project manager is confronted at the beginning of the project with the same quagmires as a few years ago [3]. The software industry’s inability to provide accurate estimates of development cost, effort, and/or time is well known [4]. Over the past few years, software development effort is found to be one of the worst estimated attributes. Significant over- or underestimates can be very expensive for company
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