Code Intelligence Hub

Academic Integrity 11 min

Emily Watson · 1 week 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 · 1 week 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 · 2 weeks 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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Case Studies 9 min

Emily Watson · 2 weeks ago

What 1200 Python CS1 Submissions Reveal About AI-Written Code Signatures

We analyzed 1200 introductory Python submissions from three semesters, applying perplexity, burstiness, and token-frequency analysis to separate human-written code from AI-generated samples. The results reveal a consistent set of statistical signatures that can catch GPT-generated and Copilot-assisted assignments—with measured false-positive rates at each threshold.

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General 9 min

Alex Petrov · 2 weeks ago

How Code Similarity Checks Catch Open Source License Violations

Code similarity analysis isn't just for catching student plagiarism. Organizations use the same techniques to identify GPL and other open source license violations in their proprietary codebases. This article walks through the algorithms, real-world cases, and practical workflows for automated license compliance auditing.

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General 8 min

James Okafor · 2 weeks 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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General 9 min

Priya Sharma · 2 weeks ago

The Measurable Impact of Static Analysis on Student Code Quality

A semester-long controlled experiment across two sections of an introductory programming course shows that students who receive automated static analysis feedback produce measurably cleaner, more maintainable code. Cyclomatic complexity dropped 22%, test coverage rose 29%, and common code smells decreased by 38%. Here’s the methodology, the data, and what it means for code-scanning in education.

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General 3 min

Alex Petrov · 2 weeks ago

Can AST Comparison Survive Student Code Obfuscation

Students often try to hide copied code by renaming variables, restructuring loops, or inserting dead code. AST-based comparison resists many of these tricks, but some deliberate obfuscation—like flattening control flow or converting recursion to iteration—can still produce a false negative. This article examines where AST engines excel, where they fall short, and how combining structural matching with token signatures catches the most clever attempts.

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Academic Integrity 10 min

Priya Sharma · 2 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%.

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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.

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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.

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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.

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