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

{"title":"LEV4REC：基于特征的工程 RSSE 方法","authors":"Claudio Di Sipio, Juri Di Rocco, Davide Di Ruscio, Phuong T. Nguyen","doi":"10.1016/j.cola.2023.101256","DOIUrl":null,"url":null,"abstract":"<div><p><span>To facilitate the development of recommender systems<span> 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 </span></span>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.</p><p>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.</p></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"78 ","pages":"Article 101256"},"PeriodicalIF":1.7000,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LEV4REC: A feature-based approach to engineering RSSEs\",\"authors\":\"Claudio Di Sipio, Juri Di Rocco, Davide Di Ruscio, Phuong T. Nguyen\",\"doi\":\"10.1016/j.cola.2023.101256\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>To facilitate the development of recommender systems<span> 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 </span></span>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.</p><p>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.</p></div>\",\"PeriodicalId\":48552,\"journal\":{\"name\":\"Journal of Computer Languages\",\"volume\":\"78 \",\"pages\":\"Article 101256\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Languages\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590118423000667\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Languages","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590118423000667","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

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。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊