I nternational Journal of E merging Trends & Technology in Computer Science (I JE TTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 1, Issue 1, May-June 2012 ISSN 2278-6856 - 62 - Abstract: Failures of software are mainly due to the faulty project management practices, which include effort estimation. Continuous changing scenarios of software development technology make effort estimation more challenging. Accurate software cost estimates are critical to both developers and customers. They can be used for generating request for proposals, contract negotiations, scheduling, monitoring and control. The exact relationship between the attributes of the effort estimation is difficult to establish. A neural network is good at discovering relationships and pattern in the data. So, in this paper we will discuss how we predict the effort using Neural Network learning techniques and a comparative analysis of different ANNs in effort estimation. Keywords: Algorithmic models, Effort Estimation, SLOC. 1. INTRODUCTION In recent years, the developments of large-scale software projects gain a growing interest. Being able to define, the software size, the development duration and the required facilities became more and more a challenging task. The reason is software architecture, requirements, tools and techniques became more complex. Software estimates are the basis for project bidding, budgeting and planning. These are critical practices in the software industry, because poor budgeting and planning often has dramatic consequences. When budgets and plans are too pessimistic, business opportunities can be lost, while over-optimism may be followed by significant losses. Software effort estimation is the process of predicting the most realistic use of effort required to develop or maintain software. Effort estimates are used to calculate effort in person-months (PM) for the Software Development work elements of the Work Breakdown Structure (WBS). Accurate Effort Estimation is important because:  It can help to classify and prioritize development projects with respect to an overall business plan.  It can be used to determine what resources to commit.  It can be used to assess the impact of changes and support re-planning.  Projects can be easier to manage and control when resources are better matched to real needs.  Customers expect actual development costs to be in line with estimated costs. 1.1 Algorithmic models Algorithmic models (AM) “calibrate” prespecified formulas for estimating development effort from historical data. Inputs to these models may include the experience of the development team, the required reliability of the software, the programming language in which the software is to be written, and an estimate of the final number of delivered source lines of code (SLOC). Basically algorithmic model is formula based model which takes historical cost information and which is based on the size of the software. Algorithmic model includes two most popular models used as follows: a) COCOMO model b) Putnam’s model and slim 1.2 ANN in Effort Estimation Artificial Neural Network is used in effort estimation due to its ability to learn from previous data. It is also able to model complex relationships between the dependent (effort) and independent variables (cost drivers). In addition, it has the ability to generalize from the training data set thus enabling it to produce acceptable result for previously unseen data. Most of the work in the application of neural network to effort estimation made use of cascade correlation and Back-propagation algorithm. Artificial Neural Network (ANN) is a massively parallel adaptive network of simple nonlinear computing elements called Neurons, which are intended to abstract and model some of the functionality of the human nervous system in Experimental Analysis of Effort Estimation Using Artificial Neural Network Anjana Bawa 1 , Mrs.Rama Chawla 2 1 MTech Student of Computer Science Department, Doon Valley Institute of Engineering and Technology, Karnal er.anjana@gmail.com 2 Asst.Professor in Computer Science Department, Doon Valley Institute of Engineering and Technology, Karnal rama_arora2002@yahoo.com