International Journal of Modeling and Optimization, Vol. 1, No. 1, April 2011 37 Abstract—It is well known fact that predicting software effort for the software development projects with any acceptable degree remains challenging. In this paper we have used the organic software projects because in each case the projects size lies between 2-50 KLOC. In this paper we have applied the linear regression model i.e. Effort = -1.5 + 0.1804 FP to predict the software project effort and function point, and on the basis of the fuzzy logic we have also predicted the software project effort. After obtaining the software effort, project manager can arrange the project progress, control the cost and ensures the quality more accurately. Indexed Terms—Software Effort, Function Point, MRE, Fuzzy Function Point, Membership Function, Mean Relative Error. I. INTRODUCTION Software development effort estimation is a branch of forecasting that has received increased interest in academia, application domains and media. Efficient development of software requires accurate estimates. Unfortunately, software development effort estimates are notorious for being too optimistic. Inaccurate software estimates causes trouble in business processes related to software development such as project feasibility analyses, budgeting and planning. It is unrealistic to expect very accurate effort estimates of software development effort because of the inherent uncertainty in software development projects, and the complex and dynamic interaction of factors that impact software development effort use. Still, it is likely that estimates can be improved because software development effort estimates are systematically overoptimistic and very inconsistent. Even small improvements will be valuable because of the large scale of software development. Software researchers have addressed the problems of effort estimation for software development projects since at least the 1960s. Research is found in areas such as [30]: 1) Creation and evaluation of estimation methods. Describes work on the creation and evaluation of Manuscript received on March 20, 2011 First Authors are with Computer Engineering Laboratory, Section of Computer Engineering, University Polytechnic, Faculty of Engineering and Technology, Jamia Millia Islamia (A Central University), New Delhi- 110025, India. E-mail: sadiq.jmi@gmail.com Second Author is the M.Tech. Scholar, Department of Computer Science and Engineering, AL-Falah School of Engineering and Technology, Dhauj, Faridabad, affiliated to Maharshi Dayanand University, Rohtak, Haryana, India. Third Author are working with the Department of Computer Science and Information Technology, Sunder Deep College of Engineering and Technology, Dasna, Ghaziabad, U.P., India, affiliated to U.P. Technical University, Lucknow, U.P, India estimation methods, such as methods based on expert judgment, structured group processes, regression-based models, simulations and neural networks. 2) Calibration of estimation models. Tailoring a model to a particular context (calibration) has been found to be difficult in practice. Problematic issues are related to, among others, when, how and how much local calibration of the models that are beneficial. 3) Software system size measures. The main input to estimation models is the size of the software to be developed. It has been proposed many size measures, for example based on the amount of functionality that is described in the requirement specification. 4) Uncertainty assessments. Software developers are typically over-confident in the accuracy of their effort estimates. Realistic uncertainty assessments are important in order to enable proper software project budgets and plans. 5) Measurement and analysis of estimation error. Proper accuracy measurement is essential when evaluating estimation methods, and identifying causes of estimation error. 6) Organizational issues related to estimation. Organizational issues such as processes to control the cost and scope of the project may have a large impact on estimation accuracy. 7) Measuring and analyzing estimation error are the basis of estimation learning related activities, such as deciding whether or not an organization has an estimation problem, identifying risk factors related to project performance in software development, and, evaluating and improving estimation and uncertainty assessment methods and tools. The most commonly used measure of estimation error is the Magnitude of Relative Error (MRE). The mean MRE (MMRE) is often used to average estimation error for multiple observations. It is not unproblematic to use MMRE as a measure of estimation accuracy, and several other measures, such as PRED and MER, is sometimes used. However, all estimation error measures have shortcomings. Hence, the measure that should be used in any given case depends on the context. The rest of the paper is organized as follows: In section 2 we have explained all the background and related work that are based on the prediction of the effort from the linear regression model and also from other techniques. Brief description about the software project effort estimation and function point analysis are given in section 3, and section 4 Prediction of Software Project Effort Estimation: A Case Study MOHD. SADIQ 1 , MOHAMMAD ASIM 2 , JAVED AHMED 1 , VINISH KUMAR 3 , SHADAB KHAN 3