Code Intelligence Hub

Academic Integrity 10 min

Priya Sharma · 3 weeks ago

Contextualizing Programming Problems to Reduce Cheating

Instead of fighting plagiarism after submissions arrive, you can design assignments that are inherently resistant to copying. By embedding unique, student-specific context into problem statements, you make it obvious when code has been copied and also harder for AI tools to produce a correct answer. This article covers concrete techniques—parameterized test cases, local data imports, and narrative hooks—that real universities have used to cut similarity rates by over 40%.

Read →

Tutorials 8 min

Emily Watson · 3 weeks ago

Automating Code Plagiarism Detection in Your Grading Workflow

A practical walkthrough for CS instructors who want to wire code similarity checks directly into their grading workflow. Covers tooling choices, LMS integration, and how to layer in web-source and AI-generated code detection for a complete academic integrity pipeline.

Read →

Academic Integrity 9 min

Alex Petrov · 3 weeks ago

How to Design Assignments That Resist Code Plagiarism

Simple changes to assignment design—unique interfaces, randomized test harnesses, and automated similarity checks—drastically reduce code plagiarism. This guide walks through six concrete tactics with real code examples and grading workflows.

Read →

Case Studies 7 min

Alex Petrov · 3 weeks ago

What 4,200 Python Submissions Tell Us About Code Reuse

By aggregating similarity scores across 4,200 student Python submissions over three semesters, we uncovered distinct copy-paste behaviors tied to assignment type, submission deadline, and language features. This practical guide walks through the exact process of running a large-scale code reuse audit using Codequiry’s output and Python data analysis, then shows how to turn those numbers into actionable course design decisions.

Read →

General 9 min

Emily Watson · 4 weeks ago

K-gram Fingerprinting for Source Code Similarity Analysis

K-gram fingerprinting is the backbone of modern code plagiarism detection. This step-by-step guide walks through tokenization, k-gram generation, hashing, winnowing, and comparison — the exact pipeline used by MOSS and Codequiry. Includes Python code examples, algorithmic tradeoffs, and real-world scaling numbers.

Read →

Tutorials 7 min

Alex Petrov · 4 weeks ago

Automated Code Similarity Checks in a CI Lab Pipeline

Setting up automated code plagiarism and similarity checks inside a CI pipeline cuts manual grading time and catches copying that individual reviewers miss. This practical guide walks through the architecture, tooling choices, and honest tradeoffs of running MOSS, JPlag, or Codequiry’s API on every lab push.

Read →

General 1 min

Emily Watson · 1 month ago

How Abstract Syntax Tree Comparison Detects Restructured Code

Abstract syntax tree (AST) comparison is a powerful technique for detecting code plagiarism that has been restructured through variable renaming, method reordering, and whitespace changes. This article explains how AST comparison works, its strengths and limitations, and when to combine it with token-based methods for best results.

Read →

General 8 min

Dr. Sarah Chen · 1 month ago

Why Some CS Departments Are Moving Beyond Moss for Plagiarism Detection

Riverdale State University’s computer science department spent years relying on Moss to catch plagiarised assignments. But as student work grew more sophisticated — combining copied web code, heavy refactoring, and AI-generated fragments — the department realised token-based similarity alone was no longer sufficient. This case study covers how they transitioned to a multi-tool detection pipeline.

Read →

General 10 min

Marcus Rodriguez · 1 month ago

What Code Fingerprinting Is and How It Catches Plagiarism

Source-code fingerprinting is the core technique behind every major plagiarism detection tool, from MOSS to Codequiry. This guide explains how it works at the algorithm level, shows you how to interpret its output, and offers practical strategies for designing assignments that resist its limitations.

Read →

Case Studies 9 min

Emily Watson · 1 month ago

How One Bootcamp Built a Code Originality Pipeline

When CareerDevs Academy scaled from 30 to 200 students per cohort, their manual code review process couldn't keep up with plagiarism and improper code reuse. Here's how they built a tiered originality pipeline combining static analysis, similarity detection, and educational intervention — and what other programs can learn from their approach.

Read →

General 11 min

Emily Watson · 2 months ago

How Static Analysis Catches Plagiarized Code Before It Ships

Plagiarism isn't just a classroom problem. When code from Stack Overflow, GitHub repos, or contractor deliverables enters your production codebase without proper attribution, you risk license violations, IP disputes, and technical debt. This guide shows how static analysis tools detect copied code before it ships, using token matching, AST comparison, and dependency scanning.

Read →

General 8 min

David Kim · 2 months ago

How Winnowing Fingerprints Resist Variable Renaming

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.

Read →