LEV4REC: A feature-based approach to engineering RSSEs

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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Abstract

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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LEV4REC:基于特征的工程 RSSE 方法
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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来源期刊
Journal of Computer Languages
Journal of Computer Languages Computer Science-Computer Networks and Communications
CiteScore
5.00
自引率
13.60%
发文量
36
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