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

SHF：媒介：协作研究：语义增强的软件可追溯性，支持以人为本的任务

• 批准号: 1901059
• 负责人: Jane Huang
• 金额: $ 101.9万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901059&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非常有益，因为可追溯性链接的底层网络可用于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难度，许多软件项目都存在可追溯性不足或不准确的问题。 这项研究将开发一个整体的交互式跟踪环境，其中结合了多种算法解决方案，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式过程，以支持各种软件工程任务。自动创建和维护准确的追溯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，让他们合作解决开放软件工程挑战，并为多元化和代表性不足的学生提供研究机会。该研究将探索具有挑战性的问题软件工程、语义文本挖掘和可视化的交叉点。 它将直接解决由于当前算法无法推理底层软件工件（例如需求、设计和代码）的深层语义而导致跟踪链接不准确的主要原因之一。 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跟踪解决方案，动态配置跟踪引擎以利用各种跟踪技术，例如语义可追溯性、跟踪链接演化和其他现有技术。最后，考虑到一组不同准确度的追踪链接，研究团队将设计、开发并公开发布一个新颖的、交互式的、可视化的界面，使用户能够了解每个链接的出处和可信度，同时提供清晰的理由该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval

利用中间工件来改进自动跟踪链接检索

• DOI: 10.1109/icsme52107.2021.00014
• 发表时间: 2021-09
• 期刊: IEEE International Conference on Software Maintenance and Evolution
• 作者: Rodriguez, Alberto D.;Cleland;Falessi, Davide
• 通讯作者: Falessi, Davide

Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions

探索开放域问答系统对最少编辑问题的对比度一致性

• 发表时间: 2023-01
• 期刊: Transactions of the Association for Computational Linguistics
• 影响因子: 10.9
• 作者: Zhang, Zhihan;Yu, Wenhao;Ning, Zheng;Ju, Mingxuan;Jiang, Meng
• 通讯作者: Jiang, Meng

Generating and visualizing trace link explanations

生成并可视化跟踪链接解释

• DOI: 10.1145/3510003.3510129
• 发表时间: 2022-05
• 期刊: IEEE Requirements Engineering Conference
• 作者: Liu, Yalin;Lin, Jinfeng;Anuyah, Oghenemaro;Metoyer, Ronald;Cleland
• 通讯作者: Cleland

Action Sequence Augmentation for Early Graph-based Anomaly Detection

用于早期基于图的异常检测的动作序列增强

• DOI: 10.1145/3459637.3482313
• 发表时间: 2021-01
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Zhao, Tong;Ni, Bo;Yu, Wenhao;Guo, Zhichun;Shah, Neil;Jiang, Meng
• 通讯作者: Jiang, Meng

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions

IfQA：反事实预设下的开放域问答数据集

• DOI: 10.18653/v1/2023.emnlp-main.515
• 发表时间: 2023-01
• 期刊: EMNLP
• 作者: Yu, Wenhao;Jiang, Meng;Clark, Peter;Sabharwal, Ashish
• 通讯作者: Sabharwal, Ashish