An Intelligent Recommender System Based on Association Rule Analysis for Requirement Engineering

Abstract

Requirement gathering is a vital step in software engineering. Even though many recent researches concentrated on the improvement of the requirement gathering process, many of their works lack completeness especially when the number of users is large. Data Mining techniques have been recently employed in various domains with promising results. In this work, we propose an intelligent recommender system for requirement engineering based on association rule analysis, which is a main category in Data Mining. Such recommender would contribute in enhancing the accuracy of the gathered requirements and provide more comprehensive results. Conducted experiments in this work prove that FP Growth outperformed Apriori in terms of execution and space consumption, while both methods were efficient in term of accuracy.