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。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。为了确保系统得到正确的实现和测试，软件工程师尝试在称为软件可追溯性的过程中将需求与代码（和其他工件）进行匹配和链接。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，可追溯过程可能既困难又耗时。该项目将开发新技术，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，来自动化软件可追溯过程。所提出的技术将使软件工程师能够以更高效和更有效的方式建立和管理软件可追溯性，最终更好地理解给定系统并更可靠地保证其按预期运行。该项目还将制作和传播有关需求工程和程序理解最佳实践的教育材料。我们希望这些材料能够融入各级教育现有的计算机素养课程中。 此外，该项目将侧重于招募和留住传统上代表性不足的类别的计算机科学学生。该项目围绕三个具体目标。 首先，它将开发能够结合（i）软件工件文本相似性的正交测量，（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，以便预测之间的准确跟踪链接软件工件。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，它将开发一种使用演化软件历史来提高跟踪链接质量的方法。 自动追溯系统的这一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为开发人员提供支持。该项目过程中开发的自动化技术将得到行业合作伙伴的彻底验证，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，并被认为是值得的。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来提供支持。

项目成果

Using Transfer Learning for Code-Related Tasks

将迁移学习用于代码相关任务

• DOI: 10.1109/tse.2022.3183297
• 发表时间: 2022-06-17
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: A. Mastropaolo;Nathan Cooper;David N. Palacio;Simone Scalabrino;D. Poshyvanyk;Rocco Oliveto;G. Bavota
• 通讯作者: G. Bavota

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

使用 BERT 模型完成代码的实证研究

• DOI: 10.1109/msr52588.2021.00024
• 发表时间: 2021-03-12
• 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
• 通讯作者: G. Bavota

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

通过实时了解重现步骤来增强移动应用程序错误报告

• DOI: 10.1109/tse.2022.3174028
• 发表时间: 2022-03-22
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: M. Fazzini;Kevin Moran;Carlos Bernal Cardenas;Tyler Wendl;A. Orso;D. Poshyvanyk
• 通讯作者: D. Poshyvanyk

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

代码注释翻译：模型有效性的比较研究

• DOI: 10.18653/v1/2021.nlp4prog-1.1
• 发表时间: 2021-06-15
• 期刊: ArXiv
• 作者: Junayed Mahmud;FAHIM FAISAL;Raihan Islam Arnob;Antonios Anastasopoulos;Kevin Moran
• 通讯作者: Kevin Moran

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

深度学习在软件工程研究中应用的系统文献综述

• DOI: 10.1145/3485275
• 发表时间: 2020-09-14
• 期刊: ACM Transactions on Software Engineering and Methodology (TOSEM)
• 作者: Cody Watson;Nathan Cooper;David Nader;Kevin Moran;D. Poshyvanyk
• 通讯作者: D. Poshyvanyk