A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Abstract

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.