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
基本信息
- 批准号:2007246
- 负责人:
- 金额:$ 50万
- 依托单位:
- 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:
- 关键词:
项目摘要
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种不同的工件,包括需求和代码。用自然语言编写的要求,规定了系统功能; 然后代码实现并测试指定的功能。为了确保已正确实施和测试系统,软件工程师试图在称为软件可追溯性的过程中匹配和链接与代码(和其他工件)的要求。不幸的是,由于基础系统的复杂性以及现代发展实践倾向于优先考虑实施功能而不是可追溯性,因此可追溯性过程既困难又耗时。该项目将通过预测开发人员的准确链接并解释为何做出这些预测来开发自动化软件可追溯性过程的新颖技术。该提出的技术将使软件工程师能够以更高效,有效的方式建立和管理软件可追溯性,最终使对给定系统的理解更好,并且更强大的保证可以按预期运行。该项目还将生产并传播有关需求工程和计划理解的最佳实践的教育材料。我们希望将这些材料纳入各个教育的现有计算机识字课程中。 此外,该项目将着重于从传统代表性不足的类别中招募和保留计算机科学专业的学生。该项目以三个特定目标为中心。 首先,它将开发出能够结合软件工件文本相似性((ii)开发人员反馈以及(iii)工件之间存在的传播链接(i)的(i)正交度量的新型技术,以预测软件工件之间的准确痕迹链接。该组件将适应机器学习,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记录来提高痕量链接质量的方法。 这种发展到自动化可追溯性系统的进化组件将适应动态统计模型技术的最新进步。最后,该项目将利用因果推断和智能代理,以帮助解释预测的痕量链接,并在痕量链接评估过程中为开发人员提供支持。在该项目过程中开发的自动化技术将通过行业合作伙伴进行彻底验证,并有望成为开发人员在建立和管理软件系统的跟踪链接方面的强大工具。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的,这是值得的。
项目成果
期刊论文数量(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 作者:Cody Watson;Nathan Cooper;David Nader-Palacio;Kevin Moran;D. Poshyvanyk
- 通讯作者:Cody Watson;Nathan Cooper;David Nader-Palacio;Kevin Moran;D. Poshyvanyk
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:
- 影响因子:0
- 作者:Junayed Mahmud;FAHIM FAISAL;Raihan Islam Arnob;Antonios Anastasopoulos;Kevin Moran
- 通讯作者:Junayed Mahmud;FAHIM FAISAL;Raihan Islam Arnob;Antonios Anastasopoulos;Kevin Moran
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:
- 影响因子:0
- 作者:Kevin Moran;Ali Yachnes;George Purnell;Juanyed Mahmud;Michele Tufano;Carlos Bernal Cardenas;D. Poshyvanyk;Zach H’Doubler
- 通讯作者:Kevin Moran;Ali Yachnes;George Purnell;Juanyed Mahmud;Michele Tufano;Carlos Bernal Cardenas;D. Poshyvanyk;Zach H’Doubler
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:
- 影响因子:0
- 作者:Matteo Ciniselli;Nathan Cooper;L. Pascarella;D. Poshyvanyk;M. D. Penta;G. Bavota
- 通讯作者:Matteo Ciniselli;Nathan Cooper;L. Pascarella;D. Poshyvanyk;M. D. Penta;G. Bavota
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:
- 影响因子:7.4
- 作者:M. Fazzini;Kevin Moran;Carlos Bernal Cardenas;Tyler Wendland;A. Orso;D. Poshyvanyk
- 通讯作者:M. Fazzini;Kevin Moran;Carlos Bernal Cardenas;Tyler Wendland;A. Orso;D. Poshyvanyk
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Denys Poshyvanyk其他文献
MASC: A Tool for Mutation-Based Evaluation of Static Crypto-API Misuse Detectors
MASC:基于突变的静态加密 API 滥用检测器评估工具
- DOI:
10.1145/3611643.3613099 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Amit Seal Ami;Syed Yusuf Ahmed;Radowan Mahmud Redoy;Nathan Cooper;Kaushal Kafle;Kevin Moran;Denys Poshyvanyk;Adwait Nadkarni - 通讯作者:
Adwait Nadkarni
Denys Poshyvanyk的其他文献
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{{ truncateString('Denys Poshyvanyk', 18)}}的其他基金
Collaborative Research: SHF: Medium: Toward Understandability and Interpretability for Neural Language Models of Source Code
合作研究:SHF:媒介:实现源代码神经语言模型的可理解性和可解释性
- 批准号:
2311469 - 财政年份:2023
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
DASS: Enabling Comprehensive and Interactive Open Source Software License Compliance
DASS:实现全面、交互式的开源软件许可证合规性
- 批准号:
2217733 - 财政年份:2022
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Medium: Bug Report Management 2.0
协作研究:SHF:中:错误报告管理 2.0
- 批准号:
1955853 - 财政年份:2020
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
EAGER: Mapping Future Synergies between Deep Learning and Software Engineering
EAGER:绘制深度学习与软件工程之间的未来协同效应
- 批准号:
1927679 - 财政年份:2019
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
SHF: Small: Natural GUI-Based Testing of Mobile Apps via Mining Software Repositories
SHF:小型:通过挖掘软件存储库对移动应用程序进行基于 GUI 的自然测试
- 批准号:
1815186 - 财政年份:2018
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CI-EN: Collaborative Research: TraceLab Community Infrastructure for Replication, Collaboration, and Innovation
CI-EN:协作研究:用于复制、协作和创新的 TraceLab 社区基础设施
- 批准号:
1510239 - 财政年份:2015
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
SHF: Small: Deep Learning Software Repositories
SHF:小型:深度学习软件存储库
- 批准号:
1525902 - 财政年份:2015
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CAREER: Enabling License Compliance Analysis and Verification for Evolving Software
职业:为不断发展的软件提供许可证合规性分析和验证
- 批准号:
1253837 - 财政年份:2013
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
Supporting student travel from underrepresented groups to the 28th IEEE International Conference on Software Maintenance (ICSM 2012)
支持代表性不足群体的学生参加第 28 届 IEEE 软件维护国际会议 (ICSM 2012)
- 批准号:
1240505 - 财政年份:2012
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
III: Small: Collaborative Research: Linking Evolving Software Requirements and Acceptance Tests
III:小:协作研究:将不断发展的软件需求和验收测试联系起来
- 批准号:
1218129 - 财政年份:2012
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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