Mutation analysis vs. code coverage in automated assessment of students' testing skills
Mutation analysis vs. code coverage in automated assessment of students' testing skills
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学生测试技能自动评估中的突变分析与代码覆盖率
DOI：
10.1145/1869542.1869567
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
O. Seppälä
中科院分区：
文献类型：
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作者：
K. Aaltonen;Petri Ihantola;O. Seppälä
学习编程应该包括学习正确的软件测试。一些自动评估系统，例如Web-CAT，允许使用覆盖率指标评估学生生成的测试套件。虽然这鼓励测试，但我们观察到，有时学生可以从高覆盖率中获得奖励，尽管他们的测试质量很差。在探索其他评估方法的过程中，我们测试了突变分析来评估学生的解决方案。将变异分析应用于真实的课程提交的初步结果表明，变异分析可以用来解决评估中的代码覆盖率问题。结合这两个指标可能会给出更准确的反馈。
Learning to program should include learning about proper software testing. Some automatic assessment systems, e.g. Web-CAT, allow assessing student-generated test suites using coverage metrics. While this encourages testing, we have observed that sometimes students can get rewarded from high coverage although their tests are of poor quality. Exploring alternative methods of assessment, we have tested mutation analysis to evaluate students' solutions. Initial results from applying mutation analysis to real course submissions indicate that mutation analysis could be used to fix some problems of code coverage in the assessment. Combining both metrics is likely to give more accurate feedback.