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Software estimation is a crucial task in software engineering. Softwareestimation encompasses cost, effort, schedule, and size. The importance ofsoftware estimation becomes critical in the early stages of the software lifecycle when the details of software have not been revealed yet. Severalcommercial and non-commercial tools exist to estimate software in the earlystages. Most software effort estimation methods require software size as one ofthe important metric inputs and consequently, software size estimation in theearly stages becomes essential. One of the approaches that has been used forabout two decades in the early size and effort estimation is called use casepoints. Use case points method relies on the use case diagram to estimate thesize and effort of software projects. Although the use case points method hasbeen widely used, it has some limitations that might adversely affect theaccuracy of estimation. This paper presents some techniques using fuzzy logicand neural networks to improve the accuracy of the use case points method.Results showed that an improvement up to 22% can be obtained using the proposedapproach.

Enhancing Use Case Points Estimation Method Using Soft Computing Techniques