CAREER: Scientist-in-the-loop-software testing

职业：科学家在环软件测试

• 批准号: 2339830
• 负责人: Joshua Sunshine
• 金额: $ 84.67万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339830&HistoricalAwards=false
• 关键词: CAREER; Scientist; loop; software; testing

Many scientists write programs every day for their work, for example, to analyze their data, automate their labs, and support collaboration. If these programs have faults, then resulting scientific results can be invalid. For example, a program fault may cause scientists to think something is true when it is not. This can lead to major theoretical or policy shifts. Scientists often learn to write software on the job, but they are rarely taught to write tests. Therefore, scientists do not follow common testing practices in professional software development. This project aims to combat faults in scientific programs by empowering scientists to efficiently create effective suites of test cases. The main thrust of the project is to conduct a set of studies to determine the underlying issues for testing in the realm of scientific programming. The new knowledge will be used to create tools that assist scientific programmers to generate suitable test suites for their software. The project integrates the research and education activities by creating new courses/curriculum directed at non-Computer Science graduate students as well as programming courses for scientific programmers that incorporate the software testing tools into the courses and continue to evaluate research hypotheses. The goal of this proposal is to help scientists write and use tests by automating away many of the challenges they currently face. The project will design and implement tailored test generation tools for scientific programmers. These tools will build on the latest innovations in program synthesis, program analysis, and testing research to support scientists in their construction of test suites. The project will design effective interaction methods for scientists to use the tools naturally and productively. The testing tool innovations will be used in education and training.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.

许多科学家每天都会为他们的工作编写程序，例如分析数据、自动化实验室和支持协作。如果这些程序有缺陷，那么产生的科学结果可能是无效的。例如，程序错误可能会导致科学家认为某件事是真的，但事实并非如此。这可能会导致重大的理论或政策转变。科学家经常在工作中学习编写软件，但很少教他们编写测试。因此，科学家们并不遵循专业软件开发中的常见测试实践。该项目旨在通过使科学家能够有效地创建有效的测试用例套件来解决科学计划中的错误。 该项目的主要目标是进行一系列研究，以确定科学编程领域测试的根本问题。新知识将用于创建工具，帮助科学程序员为其软件生成合适的测试套件。该项目通过创建针对非计算机科学研究生的新课程/课程以及针对科学程序员的编程课程（将软件测试工具纳入课程中并继续评估研究假设）来整合研究和教育活动。该提案的目标是通过自动化解决科学家目前面临的许多挑战来帮助科学家编写和使用测试。该项目将为科学程序员设计和实施定制的测试生成工具。这些工具将建立在程序综合、程序分析和测试研究方面的最新创新之上，以支持科学家构建测试套件。该项目将为科学家设计有效的交互方法，以便他们自然而高效地使用这些工具。测试工具创新将用于教育和培训。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。