Scaling Automated Programming Assessment Systems

Abstract

The first automated assessment of student programs was reported more than 60 years ago, but this topic remains relevant and highly topical among computer science researchers and teachers. In the last decade, several factors have contributed to the popularity of this approach, such as the development of massive online courses, where large numbers of students can hardly be assessed manually, the COVID-19 pandemic with a strong online presence and physical relocation of students, and the ever-increasing shortage of personnel in the field CS. Modern Automated Programming Assessment Systems (APASs) are nowadays implemented as web applications. For such web applications, especially those that support immediate (on-demand) program assessments and feedback, it can be quite a challenge to implement the various system modules in a secure and scalable manner. Over the past six years, we have developed and actively deployed “Edgar”—a state-of-the-art APAS that enables immediate program evaluation and feedback in any programming language (SQL, C, Java, etc.). In this article, we look at the APAS web application architecture with a focus on scalability issues. We review fundamental features such as dynamic analysis and untrusted code execution, as well as more complex cases such as static analysis and plagiarism detection, and we summarize the lessons learned over the previous six years of research. We identify scalability challenges, show how they have been addressed in APAS Edgar, and then propose general architectural solutions, building blocks and patterns to address those challenges.