Code Intelligence Hub

General 11 min

Alex Petrov · 1 month 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

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 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 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 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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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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