Marcus Rodriguez

General 15 min

Marcus Rodriguez • 3 days ago

What a Code Similarity Score of 85% Actually Means

A code similarity score is not a probability of cheating. It's a measurement of overlapping token fingerprints, and it can be high for honest reasons or low for dishonest ones. Here's what sits underneath the number, with worked Java examples and the thresholds that hold up when a student appeals.

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General 8 min

Marcus Rodriguez • 4 days ago

A Checklist for Verifying Contractor Code Originality

Most code originality problems arrive with an invoice attached, not a report card. Here is the six-step process I run on contracted codebases before the final payment clears, from contract language to the delivery gate that blocks a release tag.

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General 12 min

Marcus Rodriguez • 6 days ago

How Do You Set a Similarity Threshold You Can Defend in a Hearing?

A 71% similarity score means nothing on its own, but it has to survive a room full of people who want an answer. Here is the exact calibration workflow I use: normalization settings, cohort percentiles, tiered thresholds, and an evidence packet that holds up when a student appeals.

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General 8 min

Marcus Rodriguez • 1 week ago

A Review Framework for Suspected AI Code in Coursework

Most departments escalate on a single number and regret it. Here is the five-step process I hand faculty who are sitting on a pile of flagged submissions and a Friday deadline, including the two steps that settle more cases than any detector.

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General 12 min

Marcus Rodriguez • 1 week ago

How Cross-Language Code Plagiarism Slips Past Similarity Tools

I audited 1,847 programming submissions across three institutions over two terms. 3.3% were translations from another language, and automated similarity scoring caught exactly zero of them. Here is the method, the numbers, the tool comparison, and what actually surfaced the cases.

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General 10 min

Marcus Rodriguez • 2 weeks ago

A Contractor Code Provenance Checklist for CTOs

Meridian, a 40-person benefits SaaS, had 340,000 lines of contracted code and no way to answer question 34 on a diligence questionnaire. Here's the five-gate provenance audit that followed, what the tools actually found, and the clause in the MSA that would have made all of it cheap.

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General 11 min

Marcus Rodriguez • 1 month ago

How Code Plagiarism Detection Went From Hashes to LLMs

Ottenstein's 1976 detector hashed student Fortran token streams, and most of what we run today is a refined version of the same idea. This is the fifty-year arc from line diffs to winnowing, AST matching, web crawling, and statistical AI detection, plus the failure mode that still bites: a 0% similarity score that tells you nothing about authorship.

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General 11 min

Marcus Rodriguez • 1 month ago

A Framework for Verifying Code Originality From Contractors

Most statements of work say "original work" and never define it, which is how GPL code ends up in your settlement service. Here is the four-question intake review I run on every contractor deliverable, with the thresholds and tooling that hold up under scrutiny.

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General 9 min

Marcus Rodriguez • 1 month ago

How Code Plagiarism Detection Algorithms Ignore Renamed Variables

A student renames every variable and converts for loops to while loops. MOSS still flags 94%. This guide builds a minimal winnowing detector in Python 3.11 so you can see exactly why code plagiarism detection algorithms survive refactoring. We then look at where the approach breaks and how AST matching fills the gap.

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General 11 min

Marcus Rodriguez • 1 month ago

Web Code Plagiarism Detection Through Source Fingerprinting

Peer-based plagiarism checkers miss code copied from GitHub and Stack Overflow. This analysis walks through how web source fingerprinting works, what a 214-submission Java audit found, and where the approach breaks down. Includes a method comparison table and a practical review workflow.

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General 4 min

Marcus Rodriguez • 2 months 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 • 2 months 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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