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One of the most critical tasks during the software development life cycle is that of estimating the effort and time involved in the development of the software product. Estimation may be performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine learning and Analogy-based estimation. In which Analogy-based software effort estimation is the process of identifying one or more historical projects that are similar to the project being developed and then using the estimates from them. Analogy-based estimation is integrated with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle. Because of uncertainty associated with attribute measurement and data availability, fuzzy logic is introduced in the proposed model. But hardly a historical project is exactly same as the project being estimated due to some distance associated in similarity distance. This means that the most similar project still has a similarity distance with the project being estimated in most of the cases. Therefore, the effort needs to be adjusted when the most similar project has a similarity distance with the project being estimated. To adjust the reused effort, we build an adjustment mechanism whose algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic algorithm based adjustment mechanism may result to near the correct effort estimation.

An Approach for Software Effort Estimation Using Fuzzy Numbers and Genetic Algorithm to Deal with Uncertainity