In today's article we are going to explore Putnam model, a highly relevant topic that has captured the attention of many people in recent times. With a detailed and exhaustive approach, we will dive into the most important and controversial aspects related to Putnam model, with the aim of offering a complete and objective view on the topic. Through research and in-depth analysis, we will examine the implications and repercussions that Putnam model has in different areas of society, as well as possible solutions or approaches to address this issue effectively. Don't miss this opportunity to deepen your knowledge about Putnam model and gain a broader and more informed perspective on this highly relevant issue.
The Putnam model is an empirical software effort estimation model[1] created by Lawrence H. Putnam in 1978. Measurements of a software project is collected (e.g., effort in man-years, elapsed time, and lines of code) and an equation fitted to the data using regression analysis. Future effort estimates are made by providing size and calculating the associated effort using the equation which fit the original data (usually with some error).
SLIM (Software LIfecycle Management) is the name given by Putnam to the proprietary suite of tools his company QSM, Inc. developed, based on his model. It is one of the earliest of these types of models developed. Closely related software parametric models are Constructive Cost Model (COCOMO), Parametric Review of Information for Costing and Evaluation – Software (PRICE-S), and Software Evaluation and Estimation of Resources – Software Estimating Model (SEER-SEM).
A claimed advantage to this model is the simplicity of calibration.
While managing R&D projects for the Army and later at GE, Putnam noticed software staffing profiles followed the Rayleigh distribution.[2]
Putnam used his observations about productivity levels to derive the software equation:
where:
In practical use, when making an estimate for a software task the software equation is solved for effort:
An estimated software size at project completion and organizational process productivity is used. Plotting effort as a function of time yields the Time-Effort Curve. The points along the curve represent the estimated total effort to complete the project at some time. One of the distinguishing features of the Putnam model is that total effort decreases as the time to complete the project is extended. This is normally represented in other parametric models with a schedule relaxation parameter.
This estimating method is fairly sensitive to uncertainty in both size and process productivity. Putnam advocates obtaining process productivity by calibration:[1]
Putnam makes a sharp distinction between 'conventional productivity' : size / effort and process productivity.
The special skills factor, B, is a function of system size: .16 for 5-15 KSLOC, .18 for 20 KSLOC, .28 for 30 KSLOC, .34 for 40 KSLOC, .37 for 50 KSLOC and .39 for > 70 KSLOC