SHF: Medium: Collaborative Research: Semantically-Enhanced Dynamic Traceability for Supporting Human-Centric Development Tasks

SHF：中：协作研究：语义增强的动态可追溯性，支持以人为本的开发任务

• 批准号: 1901000
• 负责人: Davide Falessi
• 金额: $ 17.65万
• 依托单位: California Polytechnic State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901000&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Semantically

Achieving accurate, complete, trustworthy and usable traceability across software-intensive systems can be extremely beneficial as the underlying network of traceability links can be used to answer diverse questions about the software system and its development process. However, in practice, many software projects suffer from inadequate or inaccurate traceability due to the cost and difficulty of manually creating and maintaining trace links. This research will develop a holistic, interactive tracing environment, which incorporates diverse algorithmic solutions for dynamically generating trace links, visualizing the results, and guiding the user through the interactive process of using the results to support diverse Software Engineering tasks. Automating the creation and maintenance of accurate traceability links offers significant potential for industrial impact. For example, traceability is required by certifying bodies in safety-critical domains and can help in the construction and delivery of high quality, competitive, timely products. The cross-disciplinary nature of the team will introduce new opportunities for software engineers, data scientists, and human-computer interaction experts to collaborate in addressing open Software Engineering challenges and will provide research opportunities for diverse and underrepresented students.The research will explore challenging problems at the intersection of software engineering, semantic text mining, and visualization. It will directly address one of the prominent causes of trace-link inaccuracy caused by the inability of current algorithms to reason over deep semantics of underlying software artifacts such as requirements, design, and code. First, the researchers will investigate semantically enhanced traceability algorithms that generate trace links, even in the absence of shared textual representations. Second, the work will develop a holistic tracing solution that dynamically configures a trace engine to leverage diverse tracing techniques such as semantic traceability, trace link evolution, and other existing techniques. Finally, given a diverse set of trace links with different degrees of accuracy, the research team will design, develop, and publicly release a novel, interactive, visual interface that enables users to understand the provenance and trustworthiness of each link while providing a clear rationale for trace query results.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在软件密集型系统中实现准确、完整、可信和可用的可追溯性是非常有益的，因为可追溯性链接的底层网络可以用来回答有关软件系统及其开发过程的各种问题。 然而，在实践中，由于手动创建和维护跟踪链接的成本和困难，许多软件项目遭受不充分或不准确的可跟踪性。 本研究将开发一个整体的交互式跟踪环境，其中包含多种算法解决方案，用于动态生成跟踪链接、可视化结果并引导用户完成使用结果的交互过程以支持不同的软件工程任务。自动创建和维护准确的可追溯性链接为工业影响提供了巨大的潜力。例如，安全关键领域的认证机构需要可追溯性，并且可以帮助构建和交付高质量，有竞争力，及时的产品。 该团队的跨学科性质将为软件工程师，数据科学家和人机交互专家带来新的机会，以合作解决开放的软件工程挑战，并将为多样化和代表性不足的学生提供研究机会。该研究将探索软件工程，语义文本挖掘和可视化交叉领域的挑战性问题。 它将直接解决跟踪链接不准确的一个突出原因，这是由于当前算法无法对底层软件工件（如需求、设计和代码）的深层语义进行推理而导致的。 首先，研究人员将研究语义增强的可追溯性算法，即使在没有共享文本表示的情况下也能生成跟踪链接。其次，这项工作将开发一个整体的跟踪解决方案，动态配置跟踪引擎，以利用不同的跟踪技术，如语义可追溯性，跟踪链接的演变，和其他现有的技术。最后，给定一组具有不同准确度的多样化跟踪链接，研究团队将设计、开发并公开发布一种新颖的、互动的、该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。