Optimization of requirement prioritization using Computational Intelligence technique

Requirement Engineering (RE) is considered as an important part in Software Development Life Cycle. It is a traditional Software Engineering (SE) process. The goal of RE is to Identify, Analyze, Document and Validate requirements. Requirement Prioritization is a crucial step towards making good decisions about product plan but it is often neglected. It is observed that in many cases the product is considered as a failure without proper prioritization because it fails to meet its core objectives. When a project has tight schedule, restricted resources, and customer expectations are high then it is necessary to deploy the most critical and important features as early as possible. For this purpose requirements are prioritized. Several requirement prioritization techniques have been presented by various researchers over the past years in the domain of SE as well as Computational Intelligence. A new technique is presented in this paper which is a hybrid of both domains named as FuzzyHCV. FuzzyHCV is a hybrid of Hierarchical Cumulative Voting (HCV) and Fuzzy Expert System. Comparative analysis is performed between new technique and an existing HCV technique. Result shows that proposed technique has proved to be more reliable and accurate.