Code Intelligence Hub

General 10 min

James Okafor · 2 months ago

Scanning 9,301 Python Files for Stack Overflow Copy-Paste

A practical, code-level guide to batch-scanning Python files for web-sourced code. We walk through token normalization, fingerprinting, uploading to Codequiry, interpreting web match URLs, and stacking an AI check on flagged files. Built for CS professors auditing assignments and engineering managers verifying contractor code.

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General 11 min

Marcus Rodriguez · 2 months ago

Web Code Plagiarism Detection Through Source Fingerprinting

Peer-based plagiarism checkers miss code copied from GitHub and Stack Overflow. This analysis walks through how web source fingerprinting works, what a 214-submission Java audit found, and where the approach breaks down. Includes a method comparison table and a practical review workflow.

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General 11 min

Dr. Sarah Chen · 2 months ago

How a CS Professor Spots Refactored Code Plagiarism in Java Labs

This is the workflow Dr. Sarah Chen uses every week to catch plagiarism that survives renaming, reordering, and refactoring. It layers token normalization, AST comparison, and a smart review queue, then walks through what to look for in a diff before talking to a student.

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General 9 min

James Okafor · 2 months ago

A TA's Method for Refactoring-Resistant Plagiarism Checks

Line diffs collapse when students rename variables, reorder functions, and extract methods. Here's how token fingerprints and AST normalization catch refactored copies, and how to triage hundreds of similarity reports without drowning in false positives.

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General 6 min

Rachel Foster · 2 months ago

Automating Code Plagiarism Detection in GitHub Actions With Codequiry

Set up an automated code plagiarism detection pipeline in GitHub Actions using Codequiry's REST API. Follow precise steps to write a workflow YAML and a Python script that submits student code, receives similarity scores, flags suspicious pushes, and optionally detects AI-generated code. Includes threshold tuning, result interpretation, and false positive handling.

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General 10 min

Priya Sharma · 2 months ago

Detecting Stack Overflow Code in Student Java Submissions

Most plagiarism checkers only compare submissions against each other, so a Stack Overflow snippet with renamed variables sails through. We break down how web source matching uses token and AST fingerprints to catch code copied from Stack Overflow, GitHub, and tutorials, and show a Java example where refactoring did not hide the source.

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General 4 min

Marcus Rodriguez · 2 months ago

What 14,000 Python Submissions Reveal About AI Detection Errors

A three-semester case study at Briarwood University tracked 14,000 Python assignments through four AI code detectors. The result: false positive rates from 4% to 9% overall, spiking to 23% on common algorithmic patterns. This article breaks down the data, the code patterns that trigger false flags, and a practical workflow for balancing detection with fairness.

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General 7 min

James Okafor · 2 months ago

Teaching Web Code Plagiarism Detection With Real Student Cases

Web code plagiarism hides in plain sight when students copy from Stack Overflow, GitHub, or tutorials and rename a few variables. This post shows how to teach detection as a skill, design assignments that surface copied web code, and use a source-aware checker like Codequiry to see the evidence.

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General 11 min

Dr. Sarah Chen · 2 months ago

What Happens When a CS Course Runs Both MOSS and ChatGPT Detectors

Over 1,200 student submissions from a large public university’s introductory Python course were analyzed with Codequiry’s similarity engine and its AI code detector. The results show how traditional plagiarism tools miss a growing fraction of unauthorized work—and why layering AI detection changes what instructors actually see.

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General 11 min

Alex Petrov · 3 months ago

How a University Caught AI-Generated Code in 14Percent of CS2 Submissions

When Riverside University’s CS department ran an AI detector across 300 CS2 assignments alongside MOSS, they discovered a new layer of academic integrity challenges. The case study reveals what they found, how they calibrated thresholds, and why combining AI detection with source-code fingerprinting changed their grading workflow.

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General 11 min

James Okafor · 3 months ago

Token Fingerprinting vs AST Matching on 1,000 Refactored Java Programs

When students rename variables, extract methods, and reorder statements to hide copied code, which detection algorithm actually holds up? A controlled experiment pits winnowing, token-based matching, and AST structural hashing against a ladder of refactoring transformations — and reveals why single-technique checkers miss the cases that academic-integrity panels care about most.

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General 11 min

Marcus Rodriguez · 3 months ago

At What Point Does Token-Based Detection Fail Against Automated Refactoring?

Most plagiarism detectors rely on token streams, which break down as soon as students rename variables, reorder statements, or extract methods. We map the precise failure points, walk through AST-based recovery techniques, and show how fingerprinting fills the gaps that tree comparators leave behind. A must-bookmark deep‑dive for any CS educator or engineering lead who has watched suspect code sail right through a token‑only scanner.

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