Improving Software Testing Education through Lightweight Explicit Testing Strategies and Feedback

通过轻量级显式测试策略和反馈改进软件测试教育

基本信息

  • 批准号:
    2141923
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-07-01 至 2025-06-30
  • 项目状态:
    未结题

项目摘要

This project aims to serve the national interest by designing tools and pedagogy that will help students learn how to write better software tests. Software testing is a critical skill for computer science graduates when they enter the workforce. A software professional with better software testing skills is likely to produce more secure, more robust, and less error-prone software. Writing effective software tests is an open-ended problem that students find very challenging. The use of checklists has been shown to be an effective practice for helping students learn other software engineering skills. This project will investigate the use of checklists to provide a procedure that students can use to write software tests. Students will learn how to write software tests and use them to evaluate the quality of software they develop. The project team will develop a software tool that will run students’ tests on software and provide real-time hints and feedback on the quality of their tests. The project will also examine the benefits of testing education for students in terms of their learning outcomes and code quality. The goal of this project is to help students develop better software testing skills so that they will be successful in the computing workforce. This project will develop new data-driven methods for generating automated testing feedback and hints for software tests written by students. The project team will conduct a longitudinal study to assess the impact of using checklists and automated feedback on students’ ability to write tests using six evaluations over three years. The project will address three research questions: (1) To what extent does the checklist intervention improve students’ software testing outcomes? (2) What are the benefits of automated feedback on test coverage and quality as compared to less rigorous interventions? (3) What is the impact of writing high-quality tests on student learning outcomes? The impact of the interventions will be measured using test quality, implementation quality, improvement between pre- and post-test quiz scores, self-assessment of testing ability using a survey, and the time students spend working on each phase of their lab and homework assignments. The project has the potential to have a lasting impact on how undergraduate students learn software testing. The results of this project will be disseminated through conferences and journals that focus on computer science education. The project team will post project results including tools, experiments, and educational materials on GitHub. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.
这个项目旨在通过设计工具和教学方法来服务于国家利益,帮助学生学习如何编写更好的软件测试。对于计算机科学专业的毕业生来说,软件测试是他们进入职场的关键技能。拥有更好软件测试技能的软件专业人员可能会生产出更安全、更健壮、更不容易出错的软件。编写有效的软件测试是一个开放式的问题,学生们发现非常具有挑战性。使用核对表已被证明是帮助学生学习其他软件工程技能的有效实践。这个项目将调查核对表的使用,以提供一个学生可以用来编写软件测试的程序。学生将学习如何编写软件测试,并使用它们来评估他们开发的软件的质量。该项目团队将开发一种软件工具,该工具将运行学生对软件的测试,并提供关于测试质量的实时提示和反馈。该项目还将检查测试教育对学生的学习结果和代码质量的好处。这个项目的目标是帮助学生发展更好的软件测试技能,以便他们在计算劳动力中取得成功。这个项目将开发新的数据驱动的方法,为学生编写的软件测试生成自动化测试反馈和提示。该项目团队将进行一项纵向研究,以评估使用核对表和自动反馈对学生在三年内使用六种评估编写测试的能力的影响。该项目将解决三个研究问题:(1)检查表干预在多大程度上改善了学生的软件测试结果?(2)与不那么严格的干预相比,自动反馈在测试覆盖率和质量方面有什么好处?(3)编写高质量测试对学生学习结果的影响是什么?干预措施的影响将通过测试质量、实施质量、测试前后测试分数之间的改善、通过调查对测试能力的自我评估以及学生在实验室和家庭作业的每个阶段花费的时间来衡量。该项目有可能对本科生学习软件测试的方式产生持久的影响。该项目的成果将通过以计算机科学教育为重点的会议和期刊传播。项目团队将在GitHub上发布项目结果,包括工具、实验和教育材料。NSF IUSE:EHR计划支持研究和开发项目,以提高所有学生的STEM教育的有效性。通过参与的学生学习路径,该计划支持有前景的实践和工具的创建、探索和实施。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

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An Experience Report on Introducing Explicit Strategies into Testing Checklists for Advanced Beginners
关于将显式策略引入高级初学者测试清单的经验报告
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Kathryn Stolee其他文献

Kathryn Stolee的其他文献

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{{ truncateString('Kathryn Stolee', 18)}}的其他基金

SHF: SMALL: Automated Discovery of Cross-Language Program Behavior Inconsistency
SHF:SMALL:跨语言程序行为不一致的自动发现
  • 批准号:
    2006947
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CAREER: On the Foundations of Semantic Code Search
职业:语义代码搜索的基础
  • 批准号:
    1749936
  • 财政年份:
    2018
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
SHF: Small: Supporting Regular Expression Testing, Search, Repair, Comprehension, and Maintenance
SHF:小型:支持正则表达式测试、搜索、修复、理解和维护
  • 批准号:
    1714699
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
SHF: Medium: Collaborative Research: Semi and Fully Automated Program Repair and Synthesis via Semantic Code Search
SHF:媒介:协作研究:通过语义代码搜索进行半自动化和全自动程序修复和合成
  • 批准号:
    1645136
  • 财政年份:
    2016
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
SHF: EAGER: Collaborative Research: Demonstrating the Feasibility of Automatic Program Repair Guided by Semantic Code Search
SHF:EAGER:协作研究:展示语义代码搜索引导的自动程序修复的可行性
  • 批准号:
    1646813
  • 财政年份:
    2016
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
SHF: Medium: Collaborative Research: Semi and Fully Automated Program Repair and Synthesis via Semantic Code Search
SHF:媒介:协作研究:通过语义代码搜索进行半自动化和全自动程序修复和合成
  • 批准号:
    1563726
  • 财政年份:
    2016
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
SHF: EAGER: Collaborative Research: Demonstrating the Feasibility of Automatic Program Repair Guided by Semantic Code Search
SHF:EAGER:协作研究:展示语义代码搜索引导的自动程序修复的可行性
  • 批准号:
    1446932
  • 财政年份:
    2014
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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Improving and Developing a Connector Testing Kit for Software Frameworks
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