Knowledge Enhanced Software Repository Search

软件涉众需要经常从存储在项目存储库中的工件中找到引用，以具体化他们的想法，并在诸如影响分析、特性定位、源代码重构和软件重用等任务期间支持他们的决策。项目存储库搜索是涉众在此过程中查找相关信息的一个关键机制。在过去的十年中，研究人员采用基于关键字的文本检索方法来搜索相关工件，旨在满足干系人在这些任务中的信息需求。然而，目前的文本检索方法通常缺乏对项目目标领域的充分理解。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工件的扁平列表，其中工件之间的相互联系被忽略了。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。由于这样的限制，涉众仍然需要付出很大的努力来有效地定位和推断项目存储库中的相关工件。******本计划的主要目标是研究知识增强信息检索技术，以加强对项目利益相关者的分析和决策支持。关于目标领域的知识对于理解工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以潜在地克服当前基于关键字的方法的局限性。具体而言，我们的目标是实现三个目标：1)设计有效的工件处理技术和工具，从异构软件工件中提取领域知识；2)开发一个知识增强搜索系统，能够充分理解和处理软件工程任务中的人类查询；3)设计嵌入在搜索过程中的交互技术和相应的工具，以精炼提取的知识。******鉴于存储库搜索在软件开发中的广泛应用，我们的研究结果可以应用于支持各种项目干系人的信息需求。知识提取和工件比较的底层算法可以用来增强许多需要从工件语义中得出推论的分析工具。明确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际研究实践培训10名HQP（2名博士，3名硕士，5名学士）。所有HQP都将配备广泛的软件工程知识和自然语言处理和知识表示技术方面的专业知识。致力于目标2和3的HQP也将在以人为本的设计方面积累相当多的经验。

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.