RELATIVE ANALYSIS OF SOFTWARE COST AND EFFORT ESTIMATION TECHNIQUES BHAWANA SRIVASTAVA 1 & MANOJ WADHWA 2 1 Assistant Professor, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, India 2 Professor & HOD, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, India ABSTRACT Software effort estimation is a very critical task in the software engineering and to control quality and efficiency a suitable estimation technique is crucial. This paper gives a comparative analysis of various available software effort estimation techniques. These techniques can be widely categorised under algorithmic model, non-algorithmic model, parametric model, and machine learning models. The use of a model that accurately calculates the cost and effort of developing a software product can be a key to the success of whole development project. This paper presents a detailed analysis of several existing methods for software cost estimation. No single technique is best for all situations, and thus a careful comparison of the results of several approaches is most likely to produce realistic estimate. KEYWORDS: Software Cost Estimation, Delphi, Software Effort Estimation, COCOMO, Parametric Model, Machine Learning INTRODUCTION Software effort estimation is one of the most critical and complex, but a key activity in the software development processes. Over the last three decades, a growing trend has been observed in using variety of software effort estimation models in diversified software development processes. It is realized that the importance of all these models lies in estimating the software development costs and preparing the schedules more quickly and easily in the anticipated environments. A great amount of research time and money have been devoted to improving accuracy of the various estimation models. There is no proof on software cost estimation models to perform consistently accurate within 25% of the actual cost and 75% of the time. The accuracy of the individual models decides their applicability in the projected environments, whereas the accuracy can be defined based on understanding the calibration of the software data. Since the precision and reliability of the effort estimation is very important for the competitiveness of software companies, the enterprises and researchers have put their maximum effort to develop the accurate models to estimate effort near to accurate levels. Many estimation models have been proposed and can be categorized based on their basic formulation schemes; estimation by Non-algorithm methods expert [6], analogy based estimation schemes [6], algorithmic methods SLOC, FPA, COCOMO, SEER, SLIM including Machine Learning models like artificial neural network based approaches and fuzzy logic based estimation schemes. Accurate effort and cost estimation of software applications continue to be a critical issue for software project managers. Hence there are no best estimation methods for all different environments; they depend upon specific environment available. ESTIMATION TECHNIQUES Generally, there are many methods for software cost estimation, which are divided into four categories: Algorithmic, Non-Algorithmic, Parametric and Machine learning models. All categories is required for performing the International Journal of Computer Science and Engineering (IJCSE) ISSN 2278-9960 Vol. 2, Issue 3, July 2013, 53-68 © IASET