Code Intelligence Hub

General 14 min

Priya Sharma · 1 month 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.

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General 10 min

Rachel Foster · 1 month ago

How Few AST Nodes Do You Need to Catch a Copied Function

A single function with renamed variables, reordered statements, and changed whitespace can still look structurally identical under the hood. This step-by-step guide builds a minimal AST clone detector in Python, explains where it breaks, and shows how production tools like Codequiry stack structural, token‑level, and web‑origin checks to catch the copying that student‑grade normalizers miss.

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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 7 min

Rachel Foster · 1 month 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.

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General 9 min

James Okafor · 1 month ago

Finding Stack Overflow Code in Student Submissions With Fingerprints

A study of 5,000 Java assignments from three US universities found that nearly one in four contained code blocks directly traceable to Stack Overflow answers — yet traditional similarity checkers missed them all. We applied token-sequence fingerprinting and a web index of 1.2 million programming snippets to surface hidden web plagiarism at scale.

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General 11 min

James Okafor · 1 month 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.

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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 10 min

Marcus Rodriguez · 1 month 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.

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General 12 min

David Kim · 1 month 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.

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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 11 min

David Kim · 1 month 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.

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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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