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Emphasizing Software Quality in Undergraduate Programming Laboratories

Emphasizing Software Quality in Undergraduate Programming Laboratories
强调本科生编程实验室的软件质量
批准号:
9751194
负责人:
Matthew Dwyer
金额:
$1.11万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1997
资助国家:
美国
项目状态:
已结题
起止时间:
1997-05-15 至 1999-04-30

项目摘要

项目成果

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中文摘要
翻译
对于使用和开发软件的组织来说,软件质量正成为一个越来越重要的问题。 教育工作者需要强调质量是本科计算机科学教育的基础概念。 该项目概述了一种新的方法,称为对抗性测试,鼓励学生开发高质量的程序,并为该程序开发测试用例。 这种方法可以很容易地纳入任何涉及重要编程实验练习的课程。 在对抗性测试中,学生被自然的竞争本能所激励,试图揭露同学解决方案中的错误,同时防御同学产生的错误暴露测试案例。 这种技术引导学生更深入地了解软件中的错误来源以及构建可靠,健壮软件的不同技术。 该项目使用基于万维网的测试服务器,该服务器管理与课程和学生相关的程序和测试数据,执行测试,并积累和报告测试结果。 对抗性测试被引入计算机科学课程的五门必修课和两门选修课。 在这些课程的背景下,让学生解决编程实验室练习与对抗性测试的短期和长期影响进行了实证研究。 *
英文摘要
Software quality is becoming an increasingly important issue to organizations that use and develop software. Educators need to emphasize quality as a foundation concept in undergraduate computer science education. This project outlines a novel approach, called adversarial testing, that encourages students to develop high quality programs and to develop test cases for that program. This approach can be easily incorporated into any course that involves significant programming laboratory exercises. In adversarial testing, students are motivated by natural competitive instincts to attempt to expose faults in their classmates' solutions while at the same time defending against fault-revealing test cases produced by their classmates. This technique leads students to a deeper understanding of the sources of faults in software and of different techniques for building reliable, robust software. This project uses a World Wide Web-based testing server that manages the program and test data associated with courses and students, performs the tests, and accumulates and reports the results of testing. Adversarial testing is introduced into five required and two elective courses in the computer science curriculum. In the context of these courses, both the short-term and long-term effects of having students solve programming laboratory exercises with adversarial testing is studied empirically. *
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会议论文
SHF: Small: Distribution-aware Testing for Neural Networks
  • 批准号:
    2129824
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.85万
  • 财政年份:
    2021
  • 负责人:
    Matthew Dwyer
  • 依托单位:
FMitF: Track I: Focusing Incremental Abstraction-based Verification on Neural Networks Input Distributions
  • 批准号:
    2019239
  • 项目类别:
    Standard Grant
  • 资助金额:
    $51.0万
  • 财政年份:
    2020
  • 负责人:
    Matthew Dwyer
  • 依托单位:
SHF: Medium: Rearchitecting Neural Networks for Verification
  • 批准号:
    1900676
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $125.55万
  • 财政年份:
    2019
  • 负责人:
    Matthew Dwyer
  • 依托单位:
SHF: Small: Measurable Program Analysis
  • 批准号:
    1901769
  • 项目类别:
    Standard Grant
  • 资助金额:
    $21.97万
  • 财政年份:
    2018
  • 负责人:
    Matthew Dwyer
  • 依托单位:
海外基金