Turn up the heat!
Bob Edmison, Stephen H. Edwards · 2020
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.