A Survey on Software Cost Estimation Techniques <s> Putnam's Model <s> Application software development has been an area of organizational effort that has not been amenable to the normal managerial and cost controls. Instances of actual costs of several times the initial budgeted cost, and a time to initial operational capability sometimes twice as long as planned are more often the case than not. <s> BIB001 </s> A Survey on Software Cost Estimation Techniques <s> Putnam's Model <s> The Putnam model can be used to predict and manage software development projects. It is a management tool that takes, as input, easily obtained manpower data and produces cost and schedule estimates. This paper examines data from two real-time software projects and analyzes the applicability of the Putnam model. We propose a variation ation of the model which more reliably follows the staffing curve of real-time software applications. A critical analysis of the assumptions is presented and the parameters are reinterpreted so that they reflect the environment of embedded applications. Two projects are analyzed from actual data. It is shown how management decisions are reflected in the model. Even erratic and incomplete data can yield valuable conclusions. It is also shown that the model is appropriate to software developed with modern practices. We show how valuable management information can be obtained by laying out the data in a systematic manner. <s> BIB002 </s> A Survey on Software Cost Estimation Techniques <s> Putnam's Model <s> Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. This concern has become even more pressing as costs associated with development continue to increase. As a result, considerable research attention is now directed at gaining a better understanding of the software-development process as well as constructing and evaluating software cost estimating tools. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, Function Points, and ESTIMACS). Data on 15 large completed business data-processing projects were collected and used to test the accuracy of the models' ex post effort estimation. One important result was that Albrecht's Function Points effort estimation model was validated by the independent data provided in this study [3]. The models not developed in business data-processing environments showed significant need for calibration. As models of the software-development process, all of the models tested failed to sufficiently reflect the underlying factors affecting productivity. Further research will be required to develop understanding in this area. <s> BIB003
The Putnam's model is a dynamic multivariate model which was developed by Larry Putnam in the late 1970s for effort estimation BIB001 . The model functions by examining the many software projects and analyzing the distribution of manpower. The relationship between the size and effort is non-linear and is highly sensitive to delivery time. It provides a simple and computationally plausible way of predicting software costs. It is used to calculate both effort and time required to complete a software project based upon the specified size of the project. The Putnam's model equation is given as BIB001 : With this model, SLIM is the tool that is useful when it comes to cost estimation and allows workforce scheduling. Thus, it achieves the objectives of cost estimation as it provides the schedule and cost of the essential resources. However, it's capacity in estimating the total manpower requirements and development time at an early stage is still not satisfactory BIB002 . It has the setback of not accounting for other aspects of the software project BIB002 . There are a series of aspects relating to software development especially in its life cycle that needs to be considered. The uncertainty in the size of the software may lead to the inaccurate cost estimation. According to BIB003 SLIM's error percentage is said to be 772.87%. On the contrary, its advantage is that the model is based on two variables that are keys to cost estimation which are time and size BIB002 and it needs fewer parameters compared to COCOMO 81 and COCOMO II.