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.

Read →

General 11 min

Priya Sharma · 2 months 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.

Read →

General 14 min

Priya Sharma · 2 months ago

Thirty Years of Detecting Plagiarized Code, Then AI Arrived

When State University of Plains' CS department first faced GitHub Copilot-generated homework in 2022, their decades-old MOSS pipeline was useless. This retrospective traces their journey from manual suspicion to a layered detection stack that caught 31% of submissions as AI-generated last semester — and the hard lessons learned about false positives along the way.

Read →

General 11 min

Alex Petrov · 2 months 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.

Read →

General 7 min

Rachel Foster · 2 months ago

How UMass Amherst Brought AI Detection Into CS 121

When 800 students enroll in an introductory Python course, detecting AI-generated code by hand is impossible. UMass Amherst integrated an AI code detector trained on student-level patterns alongside traditional similarity checks—and uncovered a 14% AI flag rate, a 2% false positive rate, and a sustainable workflow that kept TAs focused on teaching rather than policing.

Read →

General 11 min

James Okafor · 2 months ago

How Perplexity-Based AI Code Detectors Actually Work

Perplexity-based detectors aren’t magic — they measure how surprising a sequence of code tokens would be to a model trained on real human code. This report breaks open the inner math, real false-positive rates from Stanford and Edinburgh benchmarks, and why the strongest detectors stack statistical signals with AST fingerprinting and web-source checks.

Read →

General 4 min

David Kim · 2 months 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.

Read →

General 10 min

Marcus Rodriguez · 2 months ago

How Perplexity and Burstiness Reveal AI-Written Code

AI code detectors don't read code—they measure its statistical shape. This piece breaks down the two key metrics, perplexity and burstiness, that separate lines from a language model from something a programmer actually typed. Real numbers, real edge cases, and how to combine signals for a higher-confidence verdict.

Read →

General 12 min

David Kim · 2 months ago

MOSS, JPlag, and AI Detectors Across 1,200 Obfuscated Submissions

A deep-dive comparison of MOSS, JPlag, Dolos, and hybrid detectors on deliberately obfuscated student Java code. Token-based algorithms catch most refactoring, but AI-generated obfuscation is changing the game — and combining similarity checks with AI detection is the only reliable way to stay ahead.

Read →

General 15 min

Dr. Sarah Chen · 2 months 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.

Read →

General 11 min

David Kim · 2 months ago

Three Semesters of Detecting Collusion in CS1 Without Burnout

When you teach 400 students each semester, “check the MOSS output” stops being a casual Friday task and becomes a logistical nightmare. After three iterations of the same introductory Java course, I’ve settled on a repeatable workflow that catches collusion without sacrificing evenings, weekends, or relationships with honest students. Here’s exactly how the pipeline works, what tools sit where, and where Codequiry finally closed the gap I was losing sleep over.

Read →

General 12 min

Dr. Sarah Chen · 2 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.

Read →