Code Intelligence Hub

General 10 min

Alex Petrov · 3 months ago

How Much Copied Stack Overflow Code Do Plagiarism Tools Actually Catch

Traditional similarity tools like MOSS and JPlag compare student submissions against each other but leave a massive blind spot: code copied directly from Stack Overflow, GitHub repositories, and online tutorials. This article examines how web source detection works, what it catches that peer comparison misses, and why both approaches together give you the real picture of code originality.

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Academic Integrity 11 min

Emily Watson · 3 months ago

Teaching Code Attribution Before Students Write a Single Line

Too many CS students treat code from Stack Overflow, GitHub, or AI tools as free for the taking. Teaching attribution as a core skill from the first assignment reduces plagiarism and builds professional habits. This article walks through concrete strategies, assignment patterns, and detection workflows that make attribution part of the learning process.

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AI Detection 9 min

Priya Sharma · 3 months ago

How Burstiness and Perplexity Catch AI-Generated Code

Burstiness and perplexity aren't just linguistic curiosities—they're the primary statistical signals that distinguish human-written source code from LLM output. This article explains exactly how these measures work under the hood, with worked examples, real-world detection rates, and honest limitations.

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Case Studies 2 min

David Kim · 3 months ago

One Community College's Web Code Plagiarism Strategy

When intro programming students at a mid-sized community college were copying entire code snippets from Stack Overflow and GitHub, the department needed a scalable detection solution. By integrating Codequiry’s web-source matching into their grading pipeline, they reduced surface-level copy-paste incidents by 40% in a single semester while cutting manual review time by 60%.

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General 8 min

James Okafor · 3 months ago

Cross-Language Code Plagiarism Detection Methods Tested

A rigorous head-to-head comparison of three cross-language code plagiarism detection approaches—tokenization, AST matching, and semantic fingerprinting—tested on 100 student-style assignments translated between Java, Python, and C++. We reveal which method catches translated loops, renamed variables, and switched control flow, and which one drowns in false positives.

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Academic Integrity 10 min

Priya Sharma · 3 months 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%.

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Tutorials 8 min

Emily Watson · 3 months 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.

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Academic Integrity 9 min

Alex Petrov · 3 months 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.

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Case Studies 7 min

Alex Petrov · 3 months 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.

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General 9 min

Emily Watson · 3 months 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.

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Tutorials 7 min

Alex Petrov · 3 months 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.

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General 1 min

Emily Watson · 3 months 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.

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