Code Intelligence Hub

General 12 min

Dr. Sarah Chen · 3 months ago

Why CS Departments Are Now Running AI Checks After Plagiarism

When Midwestern State University’s CS department discovered that MOSS alone missed nearly a third of suspicious submissions—many generated by ChatGPT—they implemented a two-stage detection pipeline. This is what they learned about running plagiarism checks first, then AI detection, and why the combination caught more than either tool alone.

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General 10 min

Priya Sharma · 3 months ago

Side-by-Side AI and Plagiarism Detection in CS2

We ran both MOSS and an AI-generated code detector on all submissions across a semester of Data Structures. What we learned about catching ChatGPT-authored code — and where MOSS still shines — changed how our department thinks about academic integrity tooling.

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General 11 min

Priya Sharma · 3 months ago

Automating Code Plagiarism and AI Checks in GitHub Classroom

GitHub Classroom automates assignment distribution, but grading still exposes copied code and AI-authored submissions late in the semester. By wiring Codequiry’s dual-purpose API into a GitHub Actions pipeline, instructors can flag both traditional plagiarism and LLM-generated solutions the moment a student pushes. This walkthrough shows the exact YAML and shell scripts you need, plus how to interpret the structured similarity and AI probability reports that come back.

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General 12 min

Marcus Rodriguez · 3 months ago

Automating Source Code Plagiarism Checks With Canvas and Codequiry

When a single CS1 assignment yields 300+ submissions, manual plagiarism checking simply doesn't scale. This hands-on guide walks through connecting Canvas to Codequiry's API, running similarity and AI-detection scans with a handful of Python scripts, and posting flagged results directly back into the SpeedGrader — so you catch the cases that matter without drowning in paperwork.

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General 13 min

Priya Sharma · 4 months ago

A Framework for Scanning AI-Generated Code in Student Submissions

AI large language models can now generate passable code for many introductory CS assignments, leaving instructors scrambling. A systematic scanning framework—combining AI detection, plagiarism analysis, and human review gates—can reliably identify AI-written submissions while respecting due process. Here’s how to build one.

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General 8 min

James Okafor · 4 months ago

How Code Similarity Detection Advanced From Strings to Semantics

From manual diff checks to AI-powered semantic analysis, code plagiarism detection has undergone a fundamental transformation. This article traces the key milestones—MOSS, JPlag, AST fingerprinting, and the new frontier of LLM-written code—and explains why a single method is no longer enough.

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General 10 min

Alex Petrov · 4 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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Case Studies 9 min

Emily Watson · 4 months 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

Priya Sharma · 4 months 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 1 min

Emily Watson · 4 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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Case Studies 9 min

Emily Watson · 4 months 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.

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Academic Integrity 9 min

Alex Petrov · 5 months ago

How Automatic Grading Evolved From Scripts to Integrity Pipelines

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.

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