  Software estimation is a crucial task in software engineering. Software
estimation encompasses cost, effort, schedule, and size. The importance of
software estimation becomes critical in the early stages of the software life
cycle when the details of software have not been revealed yet. Several
commercial and non-commercial tools exist to estimate software in the early
stages. Most software effort estimation methods require software size as one of
the important metric inputs and consequently, software size estimation in the
early stages becomes essential. One of the approaches that has been used for
about two decades in the early size and effort estimation is called use case
points. Use case points method relies on the use case diagram to estimate the
size and effort of software projects. Although the use case points method has
been widely used, it has some limitations that might adversely affect the
accuracy of estimation. This paper presents some techniques using fuzzy logic
and 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 proposed
approach.
