Code Intelligence Hub

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.

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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.

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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.

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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.

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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.

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General 12 min

Priya Sharma · 2 months 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

Priya Sharma · 2 months 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 11 min

Priya Sharma · 2 months ago

Automating Code Plagiarism and AI Checks in GitHub Classroom

GitHub Classroom automates assignment distribution, but grading still exposes copied code and AI-authored submissions late in the semester. By wiring Codequiry’s dual-purpose API into a GitHub Actions pipeline, instructors can flag both traditional plagiarism and LLM-generated solutions the moment a student pushes. This walkthrough shows the exact YAML and shell scripts you need, plus how to interpret the structured similarity and AI probability reports that come back.

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General 7 min

Rachel Foster · 2 months 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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General 8 min

James Okafor · 2 months ago

How Code Similarity Detection Grew From Diff to AI

From the early days of the Unix diff command to the rise of MOSS, JPlag, and AI-powered detectors, code similarity detection has undergone a quiet revolution. This retrospective traces the key technical milestones—tokenization, ASTs, fingerprinting, web-source matching, and the new frontier of AI-generated code—showing how each layer made plagiarism harder to hide. See how modern platforms like Codequiry unify these techniques into a single pipeline.

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General 11 min

James Okafor · 2 months ago

How an Engineering Team Automated Code Originality Checks for Contractors

When a mid-sized software firm started scaling external contractor contributions, they needed a way to verify code originality at scale. By integrating Codequiry’s API into their CI/CD pipeline, they built an automated gate that flagged plagiarized open-source snippets and AI-generated code before it ever hit the main branch. Here’s a look at their process, the false-positive tradeoffs, and the integration that saved their legal review hours.

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General 12 min

Marcus Rodriguez · 2 months ago

Automating Source Code Plagiarism Checks With Canvas and Codequiry

When a single CS1 assignment yields 300+ submissions, manual plagiarism checking simply doesn't scale. This hands-on guide walks through connecting Canvas to Codequiry's API, running similarity and AI-detection scans with a handful of Python scripts, and posting flagged results directly back into the SpeedGrader — so you catch the cases that matter without drowning in paperwork.

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