AI-Generated Code Detection: The New Frontier in Academic Integrity
As AI coding assistants become ubiquitous, learn how institutions are adapting to detect AI-generated code and maintain educational standards.
Expert insights on AI code detection and academic integrity
As AI coding assistants become ubiquitous, learn how institutions are adapting to detect AI-generated code and maintain educational standards.
Stay ahead with expert analysis and practical guides
Winnowing fingerprinting is a powerful technique for detecting code plagiarism that survives variable renaming, refactoring, and cosmetic changes. This case study examines how the algorithm works, where it succeeds, and where it falls short compared to AST-based approaches.
A retrospective on automatic grading in computer science education—from shell scripts comparing output strings to modern platforms combining unit tests, static analysis, and code similarity detection. What we gained, what we lost, and why integrity pipelines matter more than ever.
When contractors deliver source code, verifying originality and license compliance is critical. This guide walks through building an automated provenance pipeline that checks for code similarity, license violations, and proper attribution before accepting deliverables into your codebase.
Not all code similarity is plagiarism, and not all plagiarism is caught by string matching. This article breaks down the three major detection techniques—AST comparison, token-based analysis, and algorithmic fingerprinting—and explains what each one actually reveals about student submissions.
Navigating the tangled web of GNU license compliance across thousands of repositories isn't an academic exercise—it's a daily operational challenge. This profile of a senior OSPO lead reveals the tools, triage workflows, and legal nuance that keep enterprise products out of litigation.
A step-by-step guide to building a source code similarity detection pipeline from scratch. Covers tokenization, AST comparison, Winnowing fingerprinting, and heuristic scoring. Includes working Python code and configuration strategies used by universities and enterprises.
Pair programming and plagiarism can look identical to automated detectors. This article explains the technical signals that distinguish collaborative work from unauthorized code sharing, and how educators can design assignments and detection workflows that respect both academic integrity and modern development practices.
A large-scale study of 4,300 open source JavaScript repositories reveals the true nature of code copying in modern software development. The findings challenge assumptions about originality, attribution, and the tools we use to detect plagiarism.
Attribution comments are a simple but powerful tool for teaching code integrity in collaborative programming projects. This article explains how to implement them effectively, what to include, and how they transform group work from a plagiarism minefield into a learning opportunity.
An analysis of 47 open source license enforcement cases from 2008 to 2023 reveals surprising patterns: most violations aren't willful, GPL enforcement rarely goes to trial, and MIT license cases are rising faster than any other. Here's what the data says about what licenses actually enforce in practice versus what developers assume.
Cross-language code plagiarism presents a growing challenge for programming educators as students discover they can translate solutions between languages to evade detection. This article explains the techniques—AST normalization, semantic fingerprinting, and intermediate representation comparison—that modern tools use to catch these sophisticated cases.
The history of code similarity detection is a story of escalating arms races. What started with professors reading printouts has evolved through Unix diffs, token-based fingerprinting, and into modern abstract syntax tree analysis. This retrospective traces the key technical shifts that shaped how we detect code plagiarism in programming courses today.