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创建几个不同的构件，包括需求和代码。用自然语言编写的需求规定了系统功能；然后代码实现并测试指定的功能。为了确保系统得到了正确的实现和测试，软件工程师试图在一个称为软件可跟踪性的过程中将需求与代码(和其他构件)进行匹配和链接。遗憾的是，由于底层系统的复杂性以及现代开发实践倾向于将实现的功能优先于可跟踪性的事实，可跟踪性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实现软件可跟踪性过程的自动化。拟议的技术将使软件工程师能够以更有效率和更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，并更有力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方案理解的最佳做法的教育材料。我们期望将这些材料纳入各级教育中现有的计算机扫盲课程。此外，该项目将专注于从传统上代表性较低的类别中招聘和留住计算机科学专业的学生。该项目围绕三个具体目标展开。首先，它将开发新的技术，这些技术能够组合(I)软件构件的文本相似性的正交度量，(Ii)开发人员反馈，以及(Iii)存在于构件之间的传递链接，以便预测软件构件之间的准确跟踪链接。这一构成部分将采用并建立机器学习、信息检索和统计建模的技术。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。自动可追溯性系统的这一不断演变的组成部分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。在此项目过程中开发的自动化技术将得到行业合作伙伴的全面验证，并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NSF的法定使命，并通过使用基金会的智力优势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