Knowledge Enhanced Software Repository Search

软件涉众需要经常从项目存储库中存储的构件中找到引用，以具体化他们的想法，并在影响分析、功能定位、源代码重构和软件重用等任务期间支持他们的决策。项目库搜索是利益相关者在这一过程中查找相关信息的关键机制之一。在过去的十年中，研究人员采用了基于关键字的文本检索方法来搜索相关的人工产物，旨在满足利益相关者在这些任务中的信息需求。然而，目前的文本检索方法通常对项目目标领域缺乏足够的了解。这导致了搜索查询和软件构件之间存在相当大的差距。此外，这些方法的输出通常是一个扁平的构件列表，其中忽略了构件之间的互连。然而，对这些连接的推断对于执行复杂的软件工程任务是必不可少的。由于这些限制，利益相关者仍然需要付出巨大的努力来有效地定位和推断项目存储库中的相关构件。*本计划的主要目标是研究知识增强型信息检索技术，以加强对项目利益相关者的分析和决策支持。关于目标域的知识对于理解构件语义和分析它们之间的联系是必不可少的。因此，在存储库搜索期间利用这些知识可能会克服当前基于关键字的方法的局限性。具体地说，我们的目标是实现三个目标：1)设计有效的构件处理技术和工具来从异类软件构件中提取领域知识；2)开发一个知识增强型搜索系统，能够充分理解和处理软件工程任务中的人工查询；3)设计嵌入到搜索过程中的交互技术和相应的工具，以提炼提取的知识。*鉴于知识库搜索在软件开发中的广泛应用，我们的研究成果可以应用于支持不同项目利益相关者的信息需求。用于知识提取和人工产物比较的底层算法可用于增强许多分析工具，这些分析工具需要从人工产物语义中提取推理。显式的知识表示和交互技术也为支持大型项目的知识共享提供了优势。该研究项目将通过大规模软件分析的实践研究实践，培训10名HQP(2名博士、3名硕士、5名理学学士)。所有HQP都将具备丰富的软件工程知识以及自然语言处理和知识表示技术方面的专业知识。HQP在目标2和目标3上的工作还将在以人为中心的设计方面积累大量经验。

Software stakeholders need to frequently find references from artifacts stored in the project repository to concretize their ideas and to back up their decisions during tasks such as impact analysis, feature location, source code refactoring, and software reuse. Project repository search serves as one key mechanism for stakeholders to find relevant information during this process. In the past decade, researchers have adopted keyword-based text retrieval methods to search for relevant artifacts aiming to satisfy stakeholders' information needs during those tasks. However, the current text retrieval methods usually lack a sufficient understanding of the project target domain. This has resulted in a considerable gap between the search queries and the software artifacts. Moreover, the output of those methods is often a flat list of artifacts in which the interconnections among the artifacts are disregarded. Inference on those connections, however, are essential to perform complex software engineering tasks. Due to such limitations, great effort is still needed for the stakeholders to effectively locate and infer from relevant artifacts in the project repositories.******The main goal of this program is to investigate knowledge enhanced information retrieval techniques in order to strengthen the analysis and decision support for the project stakeholders. Knowledge about the target domains is indispensable for understanding the artifact semantics and for analyzing their connections. Therefore, leveraging such knowledge during repository searches can potentially overcome the limitations of current keyword-based methods. In particular, we aim to achieve three objectives: 1) design effective artifact processing techniques and tools to extract the domain knowledge from heterogeneous software artifacts; 2) develop a knowledge enhanced searching system that can adequately understand and address human queries during software engineering tasks; and 3) devise interaction techniques and corresponding tools that are embedded in the searching process to refine the extracted knowledge.******Given the wide application of repository search during software development, our research results can be applied to support the information needs of various project stakeholders. The underlying algorithms for knowledge extraction and artifact comparison can be used to enhance many analytic tools that require drawing inferences from artifact semantics. The explicit knowledge representation and interaction techniques also offer advantages to support knowledge sharing for large projects. This research program will train 10 HQP (2 Ph.D., 3 MSc, 5 BSc) through hands-on research practices in large-scale software analyses. All HQP will be well-equipped with extensive knowledge of software engineering and expertise on natural language processing and knowledge representation techniques. The HQP working on objective 2 and 3 will also develop considerable experience in human-centered design.