REU Site: Software Safety and Reliability: Research, Practice, and Innovation

REU 网站：软件安全性和可靠性：研究、实践和创新

• 批准号: 2050869
• 负责人: Weichen Wong
• 金额: $ 40.48万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050869&HistoricalAwards=false
• 关键词: REU; Site; Software; Safety; Reliability

This Research Experiences for Undergraduates (REU) site at the University of Texas at Dallas focuses on software safety and reliability, which are critical for safe and dependable operation of many systems and the reduction of devastating accidents. It offers innovative research opportunities on these important topics, especially to students from underrepresented groups or universities with limited resources. By enhancing skills through workshops and lectures in critical thinking, oral and written communication, problem solving, and research methods, students will be encouraged and well equipped for graduate education and research careers in science and technology. Interactions with mentors and fellow participants will provide avenues to exchange ideas and stimulate discussions. Research connections will be established with students' home institutions to continue collaboration among multiple institutions in different geographical regions. An REU website will publicize research articles, videos and slides of lectures and project presentations, as well as announcements for effective community outreach.The focus of this research is on evaluating the strengths and weaknesses of existing methodologies and current practices for safety assurance and reliability modeling and prediction. Issues to be explored include: how the introduction of software safety and reliability requirements may affect the software lifecycle; how software processes, techniques, and tool support should be adjusted accordingly; how safe software systems can be unreliable; how reliable software systems can be unsafe; and how to make software systems both safe and reliable. Software for blockchain-based smart contracts and autonomous systems like self-driving vehicles will be emphasized. The findings will be compiled to provide a comprehensive guideline of how software safety and reliability can be achieved effectively and efficiently. A key aspect of this site is the close collaboration with industry. REU students will take trips to industry sponsors, allowing them to speak directly with practitioners to better understand how software safety and reliability is applied to real-life applications. Workshops on technical writing and oral presentation will be held to improve proficiency in preparing and delivering technical reports. Seminars on ethics and professional responsibility will also be given to ensure REU students behave properly and respectfully.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.

德克萨斯大学达拉斯分校的本科生研究经验（REU）站点侧重于软件的安全性和可靠性，这对于许多系统的安全可靠运行和减少破坏性事故至关重要。它为这些重要主题提供了创新的研究机会，特别是对来自代表性不足的群体或资源有限的大学的学生。通过研讨会和讲座提高学生在批判性思维、口头和书面沟通、解决问题和研究方法方面的技能，学生将受到鼓励，并为研究生教育和科学技术研究事业做好准备。与导师和其他参与者的互动将提供交流思想和激发讨论的途径。将与学生所在院校建立研究联系，以继续不同地理区域的多个院校之间的合作。REU的网站将公布研究文章、讲座和项目演示的视频和幻灯片，以及有效的社区外展公告。本研究的重点是评估安全保证和可靠性建模与预测的现有方法和当前实践的优缺点。需要探讨的问题包括：引入软件安全性和可靠性需求会如何影响软件生命周期；如何相应地调整软件过程、技术和工具支持；安全的软件系统如何变得不可靠；可靠的软件系统是如何不安全的；以及如何使软件系统既安全又可靠。将强调基于区块链的智能合约和自动驾驶汽车等自动系统的软件。这些调查结果将被汇编，以提供一个全面的指导方针，说明如何有效和高效地实现软件的安全性和可靠性。该网站的一个关键方面是与工业界的密切合作。REU的学生将前往行业赞助商，使他们能够直接与从业者交谈，以更好地了解软件的安全性和可靠性如何应用于现实生活中的应用。将举办技术写作和口头报告讲习班，以提高编写和提交技术报告的熟练程度。此外，还将举办有关道德和职业责任的研讨会，以确保REU学生的行为举止得体和尊重他人。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

Social Media Safety Practices and Flagging Sensitive Posts

社交媒体安全实践和标记敏感帖子

• DOI: 10.1109/qrs-c57518.2022.00012
• 发表时间: 2022
• 期刊: and Security
• 作者: DiSalvo, Lisa M.;Saenz, Gabriela Viviana;Wong, W. Eric;Li, Dongcheng
• 通讯作者: Li, Dongcheng

Improving Search-based Automatic Program Repair with Neural Machine Translation

• DOI: 10.1109/access.2022.3164780
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Dongcheng Li;W. E. Wong;Mingyong Jian;Yi Geng;Matthew Chau

Improving Search-based Test Case Generation with Local Search using Adaptive Simulated Annealing and Dynamic Symbolic Execution

使用自适应模拟退火和动态符号执行通过本地搜索改进基于搜索的测试用例生成

• DOI: 10.1109/dsa56465.2022.00047
• 发表时间: 2022
• 期刊: 9th International Conference on Dependable Systems and Their Applications
• 作者: Li, Dongcheng;Wong, W. Eric.;Li, Shenglong;Chau, Matthew
• 通讯作者: Chau, Matthew

Automatic Test Case Generation Using Many-Objective Search and Principal Component Analysis

使用多目标搜索和主成分分析自动生成测试用例

• DOI: 10.1109/access.2022.3198694
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Li, Dongcheng;Wong, W. Eric;Pan, Sean;Koh, Liang-Seng;Li, Shenglong;Chau, Matthew
• 通讯作者: Chau, Matthew

Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle

• DOI: 10.1109/access.2021.3139534
• 发表时间: 2022-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Li, Dongcheng;Yin, Wangping;Chau, Matthew
• 通讯作者: Chau, Matthew