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

软件系统包含在各种软件工件中捕获的大量文本信息，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开发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。为了发挥作用，需要对这些技术进行适当配置，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，他们需要帮助来确定给定软件工程上下文中的最佳文本检索配置。配置问题是在软件工业中采用这些技术的主要障碍之一，因为研究人员提出的许多方法不能很好地泛化。这个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地采用文本检索。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，它将为这两个领域带来新的知识。在软件工程中使用文本检索的现有方法将变得更加实用，而不仅仅是有希望，促进从实验室到工业和学术界的迁移。本研究的结果将是：(1)一种新的方法（称为TRinSE2.0），它将实现自动的，基于运行时查询的文本检索配置；(2)改进重要的软件工程任务，在实际设置中，关注特征和错误定位、影响分析、可追溯性链接恢复和错误分类。TRinSE2.0将在开源数据、课堂和工业环境中进行评估。所提出的工作将改变软件工程应用中文本检索配置的方式。新的、特定于软件的度量，以及经过验证的基于语言的度量，将用于在软件工程任务和数据集的上下文中捕获查询属性。机器学习算法将为给定查询找到最佳配置。当编写查询从软件项目中检索信息时，开发人员将得到最好的结果，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启发式的，而是数据驱动的。

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.