Abstract
Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative filtering recommendation approach. The proposed approach will support project stakeholders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories. The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recommendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.
Highlights
Requirements engineering (RE) is a critical phase in software development
The experimental datasets, validation metrics, benchmark approaches, and experimental results of the proposed hybrid content-based collaborative filtering (HCBCF) recommendation approach are presented
To understand how prediction accuracy varies with neighborhood size and identify the optimal neighborhood size, an experiment is conducted by changing the number of neighbors from 2 to 20 and computing the corresponding Mean absolute error (MAE)
Summary
Requirements engineering (RE) is a critical phase in software development. It is an iterative process of eliciting, analyzing, specifying, validating, and managing the requirements of stakeholders. 1. An effective hybrid content-based collaborative filtering (HCBCF) recommendation approach is developed to support project stakeholders in mitigating the risk of missing requirements during requirements elicitation. 2. The HCBCF approach is a powerful and intelligent tool for reducing the effect of the information overload problem that is encountered during the process of identifying all potential related requirements of the software under development from repositories with a large number of reusable requirements. The HCBCF approach incorporates two recommendation methods: item-based CF and content-based filtering It utilizes the content information of items to enhance recommendation performance and reduce the effects of the data sparsity and cold-start item problems when adequate rating data are unobtainable.
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