Marcus Rodriguez

General 11 min

Marcus Rodriguez • 3 days 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 • 5 days 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 • 1 week 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 • 4 weeks 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 • 1 month 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 • 3 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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General 6 min

Marcus Rodriguez • 4 months ago

The 72% Illusion in Your Static Analysis Dashboard

Your static analysis dashboard is a comforting fiction. A meta-analysis of over 50 industry reports reveals a systemic 72% overstatement in reported code quality. We dissect the flawed metrics, the vendor incentives, and what engineering leaders should actually measure to prevent the next production meltdown.

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Academic Integrity 11 min

Marcus Rodriguez • 4 months ago

Your Students Are Copying Code You Can't See

A student submits a perfectly functional binary search tree. The logic is flawless, but the variable names are gibberish and the structure is bizarrely convoluted. It passes MOSS with flying colors. This is obfuscated plagiarism, the most sophisticated form of academic dishonesty in computer science. We're entering an arms race where simple token matching is no longer enough.

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General 7 min

Marcus Rodriguez • 4 months ago

Your Static Analysis Tool Is Lying to You About Code Quality

A 2024 study of 12,000 Java projects found that common static analysis metrics like cyclomatic complexity and lines of code correlate at less than 0.3 with actual maintenance costs. We're measuring the wrong things. This analysis reveals the five signals that truly matter for codebase health and why your current dashboard is probably giving you false confidence.

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