Enhance accuracy in Software cost and schedule estimation by using"Uncertainty Analysis and Assessment"in the system modeling process

Accurate software cost and schedule estimation are essential for software project success. Often it referred to as the “black art” because of its complexity and uncertainty, software estimation is not as difficult or puzzling as people think. In fact, generating accurate estimates is straightforward—once you understand the intensity of uncertainty and framework for the modeling process. The mystery to successful software estimation—distilling academic information and real-world experience into a practical guide for working software professionals. Instead of arcane treatises and rigid modeling techniques, this will guide highlights a proven set of procedures, understandable formulas, and heuristics that individuals and development teams can apply to their projects to help achieve estimation proficiency with choose appropriate development approaches In the early stage of software life cycle project manager are inefficient to estimate the effort, schedule, cost estimation and its development approach .This in turn, confuses the manager to bid effectively on software project and choose incorrect development approach. That will directly effect on productivity cycle and increase level of uncertainty. This becomes a strong cause of project failure. So to avoid such problem if we know level and sources of uncertainty in model design, It will directive the developer to design accurate software cost and schedule estimation, which are essential for software project success. However once the required efforts have estimated, little is done to recalibrate and reduce the uncertainty of the initial estimates. This paper demonstrates terminology and typology of uncertainty is presented together with a framework for the modeling process, Brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment its interaction with the broader system development process and the role of uncertainty at different stages in the modeling processes.. The applicability of these methods has been mapped according to purpose of application, stage of the modeling process and source and type of uncertainty addressed.