Marcus Rodriguez

General 4 min

Marcus Rodriguez • 2 days ago

What 14,000 Python Submissions Reveal About AI Detection Errors

A three-semester case study at Briarwood University tracked 14,000 Python assignments through four AI code detectors. The result: false positive rates from 4% to 9% overall, spiking to 23% on common algorithmic patterns. This article breaks down the data, the code patterns that trigger false flags, and a practical workflow for balancing detection with fairness.

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General 8 min

Marcus Rodriguez • 4 days ago

Grading Assignments to Detect AI-Generated Code in Student Submissions

Most AI-generated code in student submissions goes unnoticed when instructors rely on intuition or a single detector. This research-style guide explains how to layer statistical signals, peer similarity, web-source checks, and rubric design to reliably catch AI-assisted code without manufacturing false positives.

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General 1 min

Marcus Rodriguez • 6 days ago

How Greedy String Tiling Detects Plagiarized Code

Greedy string tiling is the matching algorithm behind JPlag and several modern code similarity engines. This report explains how it tokenizes source, extracts maximal contiguous matches, and why it still needs help from AST and fingerprinting to catch refactored plagiarism.

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General 11 min

Marcus Rodriguez • 1 week 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 10 min

Marcus Rodriguez • 2 weeks 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 10 min

Marcus Rodriguez • 3 weeks 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

Marcus Rodriguez • 1 month 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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General 12 min

Marcus Rodriguez • 1 month ago

A Triage Protocol for AI-Generated Code in CS Assignments

A single run of an AI detector on a suspicious student submission is not enough. CS professors need a systematic triage protocol that stacks similarity analysis, AI code detection, web-source fingerprinting, and manual review into a defensible pipeline. This article outlines a concrete workflow you can implement this semester.

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General 10 min

Marcus Rodriguez • 2 months ago

What Code Fingerprinting Is and How It Catches Plagiarism

Source-code fingerprinting is the core technique behind every major plagiarism detection tool, from MOSS to Codequiry. This guide explains how it works at the algorithm level, shows you how to interpret its output, and offers practical strategies for designing assignments that resist its limitations.

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General 8 min

Marcus Rodriguez • 3 months ago

What Pair Programming Looks Like in a Plagiarism Detector

Pair programming and plagiarism can look identical to automated detectors. This article explains the technical signals that distinguish collaborative work from unauthorized code sharing, and how educators can design assignments and detection workflows that respect both academic integrity and modern development practices.

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General 9 min

Marcus Rodriguez • 4 months ago

The Open Source Audit That Nearly Bankrupted a Startup

When a promising fintech startup sought Series B funding, their due diligence included a standard code audit. What they found wasn't a security flaw, but a legal time bomb woven into their core product. This is the story of how unmanaged open-source dependencies almost destroyed a company.

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General 3 min

Marcus Rodriguez • 4 months ago

The 8 Code Smells That Predict Your Next Plagiarism Case

Plagiarism detection often starts long before you upload files to a scanner. Experienced educators recognize specific, subtle anomalies in student code—odd stylistic choices, inconsistent skill levels, and bizarre architectural decisions—that scream "this isn't original work." Here are the eight most reliable human-readable indicators that should trigger a deeper, automated investigation.

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