一句话：利用统计故障定位为学生代码生成可疑度热图，以可视化方式引导调试。
自动化评分系统通常只反馈测试失败或运行时错误，学生若缺乏调试技能，面对缺陷常不知从何入手。本文借鉴软件工程中的统计故障定位（SFL）技术，将其用于学生编程作业：先计算代码每一行与缺陷相关的“可疑度”分数，再把该分数以热图形式叠加到源代码上。这样，学生能在不过度依赖直接答案的情况下，将注意力集中到最可能出问题的代码区域，获得调试脚手架，降低排查缺陷的摸索成本。摘要未报告具体实验评估结果。
Automated grading systems provide feedback to students in a variety of ways, but they typically focus on identifying incorrect program behaviors. Such systems provide notices of test case failures or runtime errors, but without debugging skills, students often become frustrated when they don't know where to start addressing these defects. Borrowing from work in software engineering research related to automated defect location, we leverage previous research describes using statistical fault localization (SFL) techniques to identify the probable locations of defects in student coding assignments. The goal is to use these SFL techniques to provide a scaffold for students, to direct their debugging efforts without giving too much guidance, and thus minimizing the learning associated with investigating the defects. After determining the "suspiciousness" for each line of code involved in the defect, we create a "heat map" visualization overlay onto their source code of the "suspiciousness" scores to visually guide a student's attention to parts of their code that are most likely to contain problems.
DOI 原文 ·
@inproceedings{paperbot5685,
title = {Turn up the heat!},
author = {Bob Edmison and Stephen H. Edwards},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377814.3381707}
}