Code Intelligence Hub

General 8 min

Rachel Foster · 1 month ago

AI Code Detector False Positives on Boilerplate

AI code detectors are producing false positives on the most ordinary submissions in CS1: code that looks the same because the assignment required it. This reported piece examines the data, the workflow changes instructors are making, and why combining AI detection with structural similarity reduces the error rate.

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General 6 min

Rachel Foster · 1 month 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 12 min

Priya Sharma · 1 month ago

Similarity Score Distributions Across Four Intro CS Languages

A three-semester analysis of 4,100 CS1 submissions shows why a 70% similarity score means different things in Python, Java, C++, and JavaScript. I break down percentile thresholds, boilerplate effects, false positives, and how to pair similarity checks with AI detection.

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General 10 min

Priya Sharma · 1 month 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 · 1 month 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 · 1 month 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 8 min

Marcus Rodriguez · 1 month ago

Grading Assignments to Detect AI-Generated Code in Student Submissions

Most AI-generated code in student submissions goes unnoticed when instructors rely on intuition or a single detector. This research-style guide explains how to layer statistical signals, peer similarity, web-source checks, and rubric design to reliably catch AI-assisted code without manufacturing false positives.

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General 8 min

James Okafor · 1 month ago

ChatGPT vs Copilot vs Gemini Code Detection Benchmarked

A head-to-head evaluation of AI code detection across ChatGPT-4o, GitHub Copilot, Claude 3.5 Sonnet, and Gemini 1.5 Pro. One pattern kept surfacing: text-only detectors miss refactored LLM code, while structural and multi-signal checks hold up.

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General 7 min

Rachel Foster · 1 month ago

A Hiring Manager's Audit of AI-Generated Code in Take-Home Tests

An engineering leader audit of 1,284 remote take-home coding submissions found 31.2% flagged as likely AI-generated at high confidence. Manual review confirmed 279 of 401 high-confidence flags, with a 4.5% false positive rate among high-confidence flags. Here is what the data showed and how hiring managers should handle AI detection scores.

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General 11 min

Alex Petrov · 1 month ago

How a 400-Student Python Course Flags AI and Copied Code

A 400-student Python course adopted Codequiry to check submissions for plagiarism and AI generation. The instructor found that 18% of assignments contained copy-pasted code from Chegg, and 12% showed strong signs of LLM authorship — a pattern that peer-only checks had missed entirely.

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General 11 min

Dr. Sarah Chen · 1 month 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 7 min

Dr. Sarah Chen · 1 month ago

Writing Programming Assignments That Resist Plagiarism

We cut similarity rates from 43% to 7% in a Data Structures course not by policing harder but by rewriting the assignments themselves. Here's what worked, what broke, and where detection tools like Codequiry still earn their keep.

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