Code Intelligence Hub

General 11 min

James Okafor · 1 month 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 · 1 month 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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General 11 min

Priya Sharma · 1 month ago

How Source Code Plagiarism Detection Escaped the Diff Trap

What started as a textual diff in Unix is now a high-stakes algorithmic arms race. This article traces the thirty-year evolution of source code plagiarism detection—from simple token matching and AST comparison to fingerprinting that survives variable renaming, and finally to the fresh challenge of AI-generated code. We examine the real detection rates, the tools that led each era, and where Codequiry fits as the first hybrid platform to unify peer, web, and AI checks in a single reporting workflow.

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General 10 min

Marcus Rodriguez · 1 month ago

15,000 CS Submissions Test 3 Plagiarism Detection Algorithms

Code similarity tools all promise to catch cheaters, but their underlying algorithms differ dramatically. We ran a large-scale experiment—15,000 real CS1 Java submissions, 500 manually verified suspicious pairs—to compare winnowing (MOSS), AST hashing (JPlag-style), and fingerprinting side by side. The results expose which techniques survive renaming, refactoring, and template reuse, and why a layered approach matters for low false‑positive rates in production academic workflows.

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General 11 min

Alex Petrov · 1 month ago

Putting a Code Similarity Checker in Your Git Pre-Commit Hook

A copied snippet might survive a human code review after a quick variable rename and loop inversion. A similarity checker that understands ASTs won’t be fooled. This guide walks through wiring Codequiry’s API into your Git pre‑commit workflow, step by step, so every commit is scanned for non‑original code before it hits the branch.

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General 4 min

David Kim · 1 month ago

How Web Code Plagiarism Detection Actually Works

A technical deep-dive into how modern plagiarism checkers spot code lifted from the open web. We walk through crawling, token-based fingerprinting, and matching algorithms that survive renaming and refactoring, with real examples and a look at where tools like MOSS fall short.

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General 15 min

Dr. Sarah Chen · 1 month ago

How AST-Based Similarity Catches Disguised Code Plagiarism

Token-based plagiarism detectors match sequences of tokens, but smart students can evade them by renaming variables, reordering statements, and refactoring code. Abstract Syntax Tree (AST) comparison digs deeper into the structural DNA of a program, making it far harder to disguise copied code. Learn how AST-based detection works, why it catches what MOSS and JPlag miss, and where Codequiry’s multi-layered approach fits in.

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General 12 min

Dr. Sarah Chen · 1 month 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 12 min

Priya Sharma · 1 month ago

How Purdue Northwest Caught 34% More AI-Generated Code

After years of relying on MOSS to spot peer-to-peer code plagiarism, the CS department at Purdue Northwest saw a new problem: students submitting AI-generated code that looked original to traditional checkers. By adding an AI code detector and web‑source matching to their pipeline, they flagged 34% more AI‑written assignments in a single semester — giving instructors the concrete evidence they needed to uphold academic integrity.

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General 10 min

David Kim · 1 month ago

At What Renaming Threshold Do Token-Based Detectors Fail?

Most CS instructors trust MOSS to catch code copying — but how much renaming actually breaks it? This step-by-step guide shows you how to run a controlled experiment, measure the exact threshold where token-based similarity collapses, and see why a multi-stage engine that combines tokens with AST fingerprinting catches what MOSS misses.

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General 10 min

Priya Sharma · 1 month 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 7 min

Rachel Foster · 1 month ago

Across Two Semesters, AI Code Detector Accuracy Hit 87% in Python

A two-semester experiment at a mid-sized CS department put Codequiry’s AI code detector to the test across 1,200 student submissions. The tool achieved 87% overall accuracy in identifying AI-generated Python code, with a manageable false-positive rate and strong recall. The study surfaced distinct patterns in where detection excels—and where manual judgment remains essential.

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