Computer Science – Software Engineering
Scientific paper
2011-08-06
International Journal of Software Engineering & Applications (IJSEA), Vol.2, No.3, July 2011, 95-104
Computer Science
Software Engineering
11 pages, 2 figures,1 tables
Scientific paper
Software project development process is requiring accurate software cost and schedule estimation for achieve goal or success. A lot it referred to as the "Intricate brainteaser" because of its conscience attribute which is impact by complexity and uncertainty, Generally 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 module which contribute itself process. In our everyday life, we enhance our estimation based on past experience in which problem solve by which method and in which condition and which opportune provide that method to produce better result . So, Instead of unexplained treatises and inflexible modeling techniques, this will guide highlights a proven set of procedures, understandable formulas, and heuristics that individuals and complete team can apply to their projects to help achieve estimation ability 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 require l for software project success. This paper demonstrates need of uncertainty analysis module at the modeling process for assist to recognize modular uncertainty system development process and the role of uncertainty at different stages in the modeling
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