Knowledge Enhanced Software Repository Search

知识增强软件存储库搜索

• 批准号: RGPIN-2019-05403
• 负责人: Guo, Jin
• 金额: $ 2.84万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765033
• 关键词: Knowledge; Enhanced; Software; Repository; Search

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.

软件涉众需要经常从项目存储库中存储的工件中查找引用，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备份他们的决策。项目资料库搜索是利益攸关方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的工件，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缺乏对项目目标领域的充分理解。这导致搜索查询和软件工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平面的工件列表，其中工件之间的互连被忽略。然而，对这些连接的推断对于执行复杂的软件工程任务至关重要。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项目存储库中的相关工件并从中进行推断。该计划的主要目标是研究知识增强的信息检索技术，以加强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工件语义和分析它们的连接是必不可少的。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，我们的目标有三：1）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）开发一个知识增强的搜索系统，能够充分理解和处理软件工程任务中的人工查询;以及3）设计嵌入在搜索过程中的交互技术和相应的工具，以改进所提取的知识。鉴于存储库搜索在软件开发过程中的广泛应用，我们的研究成果可以应用于支持各种项目利益相关者的信息需求。用于知识提取和工件比较的底层算法可以用于增强许多需要从工件语义中进行推断的分析工具。显式的知识表示和交互技术也提供了支持大型项目的知识共享的优势。本研究计划将培养10名HQP（2名博士，3 MSc，5 BSc）通过在大型软件分析中的实践研究。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。致力于目标2和3的HQP也将在以人为本的设计方面积累大量经验。