SHF: Small: Collaborative Research:Text Retrieval in Software Engineering 2.0

SHF：小型：协作研究：软件工程中的文本检索 2.0

• 批准号: 1526118
• 负责人: Andrian Marcus
• 金额: $ 20万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526118&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Text

Software systems contain large amounts of textual information captured in various software artifacts, such as, requirements documents, source code, user manuals, etc. The productivity of software developers and the quality of the software they produce directly depends on their ability to retrieve and understand the textual information present in software. Since humans cannot process and comprehend so much text, researchers proposed the use of text retrieval techniques to help software developers with many of their daily tasks. In order to be useful, these techniques need to be properly configured, which requires calibrating many parameters. As most software developers are not experts in text retrieval, they need help in determining the best text retrieval configuration in a given software engineering context. The configuration problem is one of the main obstacles in the adoption of such techniques in the software industry, because many approaches proposed by researchers do not generalize well. The outcomes of this project will transform the way software developers address many of their daily tasks, allowing them to easily adopt the use of text retrieval during software development. The results of this research will also be used in software engineering courses to support students in their projects. The new practices that the students will acquire will help them become better software engineers. The proposed research also brings together work from different computing research communities: software engineering and information retrieval and it will bring new knowledge in both fields. Existing approaches using text retrieval in software engineering will become more practical, rather than just promising, facilitating migration from the lab into industry and academia.The outcome of this research will be: (1) a novel approach (called TRinSE2.0), which will achieve automatic, runtime query-based text retrieval configuration; and (2) improvements to important software engineering tasks, in practical settings, focusing on feature and bug location, impact analysis, traceability link recovery, and bug triage. TRinSE2.0 will be evaluated on open source data, in the classroom, and in industrial settings. The proposed work will transform the way text retrieval configuration is done in software engineering applications. New, software-specific measures, as well as proven linguistic-based measures will be used to capture query properties in the context of software engineering tasks and data sets. Machine learning algorithms will find the best configuration for a given query. When writing a query to retrieve information from a software project, developers will get the best results, saving them time and effort, improving their productivity and the quality of their work. The text retrieval configuration problem will no longer be heuristic-based, but it will become data-driven.

软件系统包含了大量的文本信息，这些文本信息被捕获在各种软件工件中，例如需求文档、源代码、用户手册等。软件开发人员的生产力和他们生产的软件的质量直接取决于他们检索和理解软件中存在的文本信息的能力。 由于人类无法处理和理解如此多的文本，研究人员提出使用文本检索技术来帮助软件开发人员完成许多日常任务。 为了有用，这些技术需要适当配置，这需要校准许多参数。 由于大多数软件开发人员都不是文本检索方面的专家，因此他们需要帮助来确定给定软件工程环境中的最佳文本检索配置。 配置问题是软件行业采用此类技术的主要障碍之一，因为研究人员提出的许多方法都不能很好地推广。 该项目的成果将改变软件开发人员处理许多日常任务的方式，使他们能够在软件开发过程中轻松采用文本检索。 这项研究的结果也将用于软件工程课程，以支持学生在他们的项目。 学生将获得的新实践将帮助他们成为更好的软件工程师。 拟议的研究还汇集了来自不同计算研究社区的工作：软件工程和信息检索，它将在这两个领域带来新的知识。 现有的在软件工程中使用文本检索的方法将变得更加实用，而不仅仅是有前途，促进从实验室迁移到工业和学术界。（称为TRinSE2.0），它将实现自动的、运行时基于查询的文本检索配置;以及（2）在实际环境中对重要软件工程任务的改进，重点是功能和错误定位、影响分析、可追溯性链接恢复和错误分类。 TRinSE2.0将在课堂和工业环境中对开源数据进行评估。 所提出的工作将改变文本检索配置在软件工程应用中的方式。 新的，特定于软件的措施，以及经过验证的基于语言的措施将被用来捕捉查询属性的软件工程任务和数据集的背景下。 机器学习算法将为给定的查询找到最佳配置。 在编写查询以从软件项目中检索信息时，开发人员将获得最佳结果，节省他们的时间和精力，提高他们的生产力和工作质量。 文本检索配置问题将不再是基于知识的，而是数据驱动的。