Enhancing Use Case Points Estimation Method Using Soft Computing Techniques

TL;DR

This paper enhances the use case points software estimation method by integrating fuzzy logic and neural networks, significantly improving accuracy in early project size and effort predictions.

Contribution

It introduces novel soft computing techniques to improve the accuracy of the traditional use case points estimation method.

Findings

Up to 22% improvement in estimation accuracy.

Fuzzy logic and neural networks effectively enhance traditional methods.

Early size and effort estimation accuracy is significantly increased.

Abstract

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.