LEV4REC：基于特征的工程 RSSE 方法

IF 1.7 3区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Computer Languages Pub Date : 2023-12-18 DOI:10.1016/j.cola.2023.101256

Claudio Di Sipio, Juri Di Rocco, Davide Di Ruscio, Phuong T. Nguyen

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引用次数: 0

摘要

为了促进软件工程推荐系统（RSSE）的开发，本文介绍了 LEV4REC，这是一种模型驱动方法，支持从设计到部署的所有 RSSE 开发阶段。它可以进行参数微调，通过使用专用功能模型进行早期配置来增强开发人员和用户的体验。我们将 LEV4REC 应用于两个基于不同算法的现有 RSSE，对其进行了评估。结果表明，LEV4REC 能够重新创建合适的建议，并优于最先进的方法。焦点小组的定性研究结果进一步验证了 LEV4REC 的有效性，同时也表明需要扩展点来支持其他系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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LEV4REC: A feature-based approach to engineering RSSEs

To facilitate the development of recommender systems for software engineering (RSSEs), this paper introduces LEV4REC, a model-driven approach supporting all RSSE development stages, from design to deployment. It enables parameter fine-tuning, enhancing the developer and user experience by using a dedicated feature model for early configuration. We evaluated LEV4REC by applying it to two existing RSSEs based on different algorithms.

Results demonstrate its ability to recreate suitable recommendations and outperform a state-of-the-art approach. Qualitative findings from a focus group study further validate LEV4REC’s effectiveness, while indicating the need for extension points to support additional systems.

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来源期刊

Journal of Computer Languages Computer Science-Computer Networks and Communications

CiteScore

5.00

自引率

13.60%

发文量