Code plagiarism continues to challenge academic institutions, especially in computer science courses. Students often copy code from online sources or classmates, assuming that small changes—like renaming variables or tweaking loops—will make it undetectable. Tools like MOSS have long helped catch such cases, but they have their limitations.
Moss Stanford works well for comparing student submissions within a class, focusing on structural similarities rather than formatting. It can detect reworded or partially copied code but struggles with AI-generated submissions or code taken from online repositories. Moreover, it doesn’t scan the web, leaving a gap in detection capabilities.
Codequiry enhances plagiarism detection by going beyond peer comparison. It uses semantic analysis and scans billions of online sources, identifying both direct copying and disguised similarities. It can detect subtle plagiarism, including AI-written code, restructured logic, and reused code snippets across various languages like Python, Java, and C++.
While MOSS remains a helpful tool, Codequiry stands out as a smarter, more complete code similarity checker for modern classrooms. It not only enhances detection accuracy but also supports fair grading and upholds academic honesty in today’s evolving digital learning environment.