Learning to Recognize Semantically Similar Program Statements in Introductory Programming Assignments
Learning to Recognize Semantically Similar Program Statements in Introductory Programming Assignments
复制标题
学习识别入门编程作业中语义相似的程序语句
DOI：
10.1145/3408877.3439599
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Rivero, Carlos R.
中科院分区：
文献类型：
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作者：
Jawalkar, Mayur Sunil;Hosseini, Hadi;Rivero, Carlos R.
随着参加编程入门课程的学生人数不断增加，教师面临着提供及时和定性反馈的挑战。自动化系统在解决可扩展性问题和为学生提供个性化反馈方面很有吸引力。当前的许多方法无法处理灵活的评分方案和关于（一组）程序语句的低级反馈。将程序依赖图形式的程序静态分析与近似图比较相结合有望解决以前的缺点。目前的技术需要对学生项目进行两两比较，这在实践中是无法扩展的。我们探索了能够识别未见的程序语句是否属于语义相似的程序语句集的模型的技术。我们在公开的入门编程作业上的初步结果表明，可以高精度地将单个程序语句分配给一些流行的语义相似的集合，并且这些集合覆盖了很大比例，这表明教师提供的反馈可以自动传播到其他学生项目中。
With the continuously increasing population of students enrolling in introductory programming courses, instructors are facing challenges to provide timely and qualitative feedback. Automated systems are appealing to address scalability issues and provide personalized feedback to students. Many of the current approaches fail to handle flexible grading schemes and low-level feedback regarding (a set of) program statements. The combination of program static analysis in the form of program dependence graphs and approximate graph comparisons is promising to address the previous shortcomings. Current techniques require pairwise comparisons of student programs that does not scale in practice. We explore techniques to learn models that are able to recognize whether an unseen program statement belong to a semantically-similar set of program statements. Our initial results on a publicly-available introductory programming assignment indicate that it is possible to assign with high accuracy an individual program statement to some of the popular semantically-similar sets, and a large proportion is covered with these, which suggests feedback provided by instructors can be automatically propagated to other student programs.
入学人数激增：调查数据
DOI：
10.1145/2839509.2844663
发表时间：
2016
期刊：
Proceedings of the 47th ACM Technical Symposium on Computing Science Education
影响因子：
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作者：
T. Camp;S. Zweben;D. Buell;Jane Stout
通讯作者：
Jane Stout
当我们给课程评分时我们在想什么？
DOI：
10.1145/2445196.2445339
发表时间：
2013
期刊：
Proceedings of the 47th ACM Technical Symposium on Computing Science Education
影响因子：
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作者：
Sue Fitzgerald;Brian F. Hanks;R. Lister;R. McCauley;Laurie Murphy
通讯作者：
Laurie Murphy