Supporting Software Developers with a Holistic Recommender System

Luca Ponzanelli, Simone Scalabrino, G. Bavota, Andrea Mocci, R. Oliveto, M. D. Penta, Michele Lanza
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引用次数: 41

Abstract

The promise of recommender systems is to provide intelligent support to developers during their programming tasks. Such support ranges from suggesting program entities to taking into account pertinent Q&A pages. However, current recommender systems limit the context analysis to change history and developers' activities in the IDE, without considering what a developer has already consulted or perused, e.g., by performing searches from the Web browser. Given the faceted nature of many programming tasks, and the incompleteness of the information provided by a single artifact, several heterogeneous resources are required to obtain the broader picture needed by a developer to accomplish a task. We present Libra, a holistic recommender system. It supports the process of searching and navigating the information needed by constructing a holistic meta-information model of the resources perused by a developer, analyzing their semantic relationships, and augmenting the web browser with a dedicated interactive navigation chart. The quantitative and qualitative evaluation of Libra provides evidence that a holistic analysis of a developer's information context can indeed offer comprehensive and contextualized support to information navigation and retrieval during software development.
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用整体推荐系统支持软件开发人员
推荐系统的承诺是在开发人员的编程任务期间为他们提供智能支持。这种支持的范围从建议程序实体到考虑相关的问答页面。然而,当前的推荐系统将上下文分析限制在更改IDE中的历史和开发人员的活动,而不考虑开发人员已经咨询或阅读的内容,例如,通过从Web浏览器执行搜索。考虑到许多编程任务的多面性,以及单个工件提供的信息的不完整性,需要几个异构资源来获得开发人员完成任务所需的更广泛的图像。我们提出Libra,一个全面的推荐系统。它通过构建开发人员所阅读的资源的整体元信息模型,分析它们的语义关系,并使用专用的交互式导航图来增强web浏览器,从而支持搜索和导航所需信息的过程。Libra的定量和定性评估证明,对开发人员信息上下文的整体分析确实可以为软件开发过程中的信息导航和检索提供全面和情境化的支持。
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