CAREER: Management of Unstructured Information During Software Evolution

软件是由大量工件组成的；其中一些旨在由编译器读取，而许多其他旨在由开发人员读取。以用户为中心的信息通常用自然语言表达，并且通常比源代码大得多。鉴于现有软件系统中存在大量的非结构化数据，在将其交付给用户之前，需要使用工具来存储、检索和分析这些数据。当开发人员需要它来理解软件时，这种类型的信息在软件发展过程中是必不可少的。该研究将定义和评估软件系统中文本信息管理的基础设施。该基础设施将利用信息检索技术，结合统计和基于规则的自然语言处理方法和其他文本分析技术。基础设施将被用来定义一种新的软件系统概念模型，它将补充结构、行为和架构模型，这些模型可以用传统的分析和建模方法提取和构建。新的概念模型和基础结构将用于定义新的方法和构建工具，以支持各种软件演进任务，例如：软件中的变更传播、软件工件之间的可追溯性链接恢复、错误和变更预测、质量度量、概念定位、重构和一般的程序理解。计划中的基础设施将为来自不同计算机科学领域（如软件工程和计算语言学）的研究人员提供一个平台，以使用来自每个领域的最新成果。经验性的工作将产生软件工件和软件文本性质的分析数据的存储库，用于支持研究社区中严格控制的实验和基准测试。

Software is comprised of a multitude of artifacts; some of them are intended to be read by the compiler, while many others are intended to be read by developers. The user centric information is often expressed in natural language and it is usually much larger in size than the source code. Given the amount of unstructured data present in existing software systems, tools are necessary for its storage, retrieval, and analysis, before it is delivered to the users. This type of information is essential during software evolution, when developers need it to understand the software.The research will define and evaluate an infrastructure for the management of the textual information present in software systems. The infrastructure will make use of Information Retrieval techniques in combination with statistical and rule-based Natural Language Processing methods and other text analysis techniques. The infrastructure will be used do define a new type of conceptual model of a software system that will complement the structural, behavioral, and architectural models, which can be extracted and built with traditional analysis and modeling methods. The new conceptual model and infrastructure will be used to define novel methodologies and build tools to support a variety of software evolution tasks, such as: change propagation in software, traceability link recovery between software artifacts, error and change prediction, quality measurement, concept location, refactoring, and program comprehension in general. The planned infrastructure will offer a platform for researchers from different areas of computer science (such as, software engineering and computational linguistics) to use state of the art results from each field. The empirical work will result in a repository of software artifacts and analysis data of textual nature from software to be used to support rigorously controlled experimentation and benchmarking in the research community.