An Intelligent Tutoring System for API Misuse Correction by Instant Quality Feedback
Rui Zhao, Harvey P. Siy, Chulwoo Pack, Leen‐Kiat Soh, Myoungkyu Song · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · 2022
Computer science students have difficulty understanding correct usages of an Application Programming Interface (API) and programming violations that cause compilation or runtime errors. Despite high-quality documentation for programming, the students typically need an instructor's feedback when their programs cause bugs, crashes, and vulnerabilities. This paper presents a pedagogical approach that is based on an Intelligent Tutoring System called INTTuToR. Briefly, INTTUTORprovides novice students with instant feedback to fix their programming issues or vulnerabilities. We have implemented our approach as a plug-in application in the Integrated Development Environment (IDE) for an interactive educational environment. In our proposed evaluation, we plan to perform empirical studies with CS students to assess how effectively INTTUTORimproves their ability to identify and fix potential bugs or vulnerabilities in the cryptography-related programming assignments.