Emily Watson

General 9 min

Emily Watson • 10 hours ago

Telling AI-Generated Code Apart From Peer Copying

When a whole class prompts the same model, peer similarity scores climb even though nobody copied anybody. Here is how to read an AI detection score, a clustering pattern, and a genuine copy pair as three different things, and how I triage 200 submissions in under two hours without accusing the wrong students.

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General 14 min

Emily Watson • 1 month ago

Why CS Professors Are Adding Web-Source Checks to Plagiarism Workflows

Web code plagiarism is distinct from peer copying, and standard similarity tools miss it. This guide breaks down how to detect code lifted from Stack Overflow, GitHub, and tutorials, with a practical grading workflow that catches it without drowning TAs in false flags.

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General 11 min

Emily Watson • 1 month ago

Automating Code Plagiarism Checks in GitHub Actions

A practical walkthrough for CS instructors and TAs: wire Codequiry's peer similarity and AI detection into a GitHub Actions workflow, get CSV results on every commit, and triage suspicious submissions in under a minute each.

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General 1 min

Emily Watson • 1 month ago

How a CS2 TA Screens ChatGPT-Generated Python Before Grading

Most ChatGPT-generated Python doesn't announce itself with a watermark. It announces itself in docstrings that restate the function name, broad exception blocks, and comments that narrate the obvious. A CS2 TA's 20-minute screen, combining Codequiry's AI detector and similarity checks, catches the majority of cases before the first grade is entered.

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General 11 min

Emily Watson • 2 months ago

Scanning a Codebase for GPL Violations Before Acquisition

When ZephyrCloud faced a pre-acquisition license audit, its engineering team turned to automated code similarity scanning after manual searches proved unreliable. The process uncovered several GPL-licensed snippets copied from Stack Overflow and GitHub, forcing a careful remediation effort that saved the deal. Here’s what they learned about using plagiarism detection for open-source compliance.

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Academic Integrity 11 min

Emily Watson • 3 months ago

Teaching Code Attribution Before Students Write a Single Line

Too many CS students treat code from Stack Overflow, GitHub, or AI tools as free for the taking. Teaching attribution as a core skill from the first assignment reduces plagiarism and builds professional habits. This article walks through concrete strategies, assignment patterns, and detection workflows that make attribution part of the learning process.

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Case Studies 9 min

Emily Watson • 3 months ago

What 1200 Python CS1 Submissions Reveal About AI-Written Code Signatures

We analyzed 1200 introductory Python submissions from three semesters, applying perplexity, burstiness, and token-frequency analysis to separate human-written code from AI-generated samples. The results reveal a consistent set of statistical signatures that can catch GPT-generated and Copilot-assisted assignments—with measured false-positive rates at each threshold.

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Tutorials 8 min

Emily Watson • 3 months ago

Automating Code Plagiarism Detection in Your Grading Workflow

A practical walkthrough for CS instructors who want to wire code similarity checks directly into their grading workflow. Covers tooling choices, LMS integration, and how to layer in web-source and AI-generated code detection for a complete academic integrity pipeline.

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General 9 min

Emily Watson • 3 months ago

K-gram Fingerprinting for Source Code Similarity Analysis

K-gram fingerprinting is the backbone of modern code plagiarism detection. This step-by-step guide walks through tokenization, k-gram generation, hashing, winnowing, and comparison — the exact pipeline used by MOSS and Codequiry. Includes Python code examples, algorithmic tradeoffs, and real-world scaling numbers.

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General 1 min

Emily Watson • 3 months ago

How Abstract Syntax Tree Comparison Detects Restructured Code

Abstract syntax tree (AST) comparison is a powerful technique for detecting code plagiarism that has been restructured through variable renaming, method reordering, and whitespace changes. This article explains how AST comparison works, its strengths and limitations, and when to combine it with token-based methods for best results.

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Case Studies 9 min

Emily Watson • 3 months ago

How One Bootcamp Built a Code Originality Pipeline

When CareerDevs Academy scaled from 30 to 200 students per cohort, their manual code review process couldn't keep up with plagiarism and improper code reuse. Here's how they built a tiered originality pipeline combining static analysis, similarity detection, and educational intervention — and what other programs can learn from their approach.

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General 11 min

Emily Watson • 4 months ago

How Static Analysis Catches Plagiarized Code Before It Ships

Plagiarism isn't just a classroom problem. When code from Stack Overflow, GitHub repos, or contractor deliverables enters your production codebase without proper attribution, you risk license violations, IP disputes, and technical debt. This guide shows how static analysis tools detect copied code before it ships, using token matching, AST comparison, and dependency scanning.

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