SHF: Small: Towards a Holistic Causal Model for Continuous Software Traceability

SHF：小型：迈向连续软件可追溯性的整体因果模型

• 批准号: 2007246
• 负责人: Denys Poshyvanyk
• 金额: $ 50万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007246&HistoricalAwards=false
• 关键词: SHF; Small; Towards; Holistic; Causal

The construction of a software system leads to the creation of several different artifacts, including requirements and code. Requirements, written in natural language, stipulate the system functionality; code then implements and tests the specified functionality. To ensure that a system has been properly implemented and tested, software engineers attempt to match and link requirements to code (and other artifacts) in a process known as software traceability. Unfortunately, the traceability process can be both difficult and time consuming due to the complexity of the underlying system and the fact that modern development practices tend to prioritize implemented functionality over traceability. This project will develop novel techniques for automating the software traceability process by predicting accurate links for developers and explaining why these predictions were made. The proposed techniques will allow software engineers to establish and manage software traceability in a more efficient and effective manner, ultimately leading to a better understanding of a given system and more robust guarantees that it is functioning as intended. The project will also produce and disseminate educational materials on best practices for requirements engineering and program comprehension. We expect these materials to be integrated into existing computer literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories.The project is centered on three specific goals. First, it will develop novel techniques that are capable of combining (i) orthogonal measures of the textual similarity of software artifacts, (ii) developer feedback, and (iii) transitive links that exist between artifacts, in order to predict accurate trace links between software artifacts. This component will adapt and build upon techniques for machine learning, information retrieval, and statistical modeling. Second, it will develop a method for using evolutionary software histories to improve trace-link quality. This evolutionary component to the automated traceability system will adapt recent advancements in dynamic statistical-modeling techniques. Finally, the project will leverage causal inference and intelligent agents to aid in explaining predicted trace links and supporting developers in the trace-link evaluation process. The automated techniques developed during the course of this project will be thoroughly validated with industry partners, and are expected to become a powerful tool for developers in establishing and managing trace links for software systems.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.

软件系统的构建导致了几个不同工件的创建，包括需求和代码。需求，用自然语言编写，规定了系统的功能；然后代码实现并测试指定的功能。为了确保系统被正确地实现和测试，软件工程师试图在一个称为软件可追溯性的过程中匹配和链接需求到代码（和其他工件）。不幸的是，由于底层系统的复杂性，以及现代开发实践倾向于优先考虑已实现的功能而不是可追溯性，跟踪过程可能既困难又耗时。这个项目将开发新的技术，通过为开发人员预测准确的链接，并解释为什么做出这些预测，来自动化软件跟踪过程。所建议的技术将允许软件工程师以更高效和有效的方式建立和管理软件可追溯性，最终导致对给定系统的更好理解，并更可靠地保证其按预期运行。该项目还将制作和传播关于需求工程和程序理解的最佳实践的教育材料。我们期望这些材料能被整合到现有的各级教育的计算机知识课程中。此外，该项目将侧重于从传统上代表性不足的类别中招募和留住计算机科学专业的学生。该项目以三个具体目标为中心。首先，它将开发能够结合(i)软件工件文本相似度的正交度量，（ii）开发人员反馈，以及（iii）工件之间存在的传递链接的新技术，以便预测软件工件之间的准确跟踪链接。该组件将适应并建立在机器学习、信息检索和统计建模技术的基础上。其次，它将开发一种使用进化软件历史来提高跟踪链接质量的方法。自动跟踪系统的这种进化组件将适应动态统计建模技术的最新进展。最后，该项目将利用因果推理和智能代理来帮助解释预测的跟踪链接，并在跟踪链接评估过程中支持开发人员。在这个项目的过程中开发的自动化技术将被工业伙伴彻底验证，并且有望成为开发人员建立和管理软件系统跟踪链接的强大工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

• DOI: 10.1145/3485275
• 发表时间: 2020-09
• 期刊: ACM Transactions on Software Engineering and Methodology (TOSEM)
• 作者: Cody Watson;Nathan Cooper;David Nader-Palacio;Kevin Moran;D. Poshyvanyk

An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation

• DOI: 10.1109/saner53432.2022.00069
• 发表时间: 2022-03
• 期刊: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
• 作者: Kevin Moran;Ali Yachnes;George Purnell;Juanyed Mahmud;Michele Tufano;Carlos Bernal Cardenas;D. Poshyvanyk;Zach H’Doubler

Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

• DOI: 10.18653/v1/2021.nlp4prog-1.1
• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Junayed Mahmud;FAHIM FAISAL;Raihan Islam Arnob;Antonios Anastasopoulos;Kevin Moran

Enhancing Mobile App Bug Reporting via Real-Time Understanding of Reproduction Steps

• DOI: 10.1109/tse.2022.3174028
• 发表时间: 2022-03
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: M. Fazzini;Kevin Moran;Carlos Bernal Cardenas;Tyler Wendland;A. Orso;D. Poshyvanyk

An Empirical Study on the Usage of BERT Models for Code Completion

• DOI: 10.1109/msr52588.2021.00024
• 发表时间: 2021-03
• 期刊: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)
• 作者: Matteo Ciniselli;Nathan Cooper;L. Pascarella;D. Poshyvanyk;M. D. Penta;G. Bavota