Rachel Foster

General 17 min

Rachel Foster • 13 hours ago

Teaching Open Source License Compliance Across the CS Curriculum

Most CS departments teach students to cite what they copy but never to read the license attached to it. This reported guide walks through the licenses that actually show up in student repositories, the four failure modes that recur in grading, and a provenance rubric that separates academic honesty from legal obligation.

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General 9 min

Rachel Foster • 1 week ago

Token, AST, and Fingerprint Matching on Refactored Student Code

Renaming a variable, extracting a helper, and swapping a for loop for a while loop are the three moves students reach for when they want a copied submission to look original. Some similarity engines shrug them off, and some lose the match entirely. This piece walks through how token hashing, AST subtree matching, and fingerprinting each behave against a deliberately refactored Python pair, then compares what MOSS, JPlag, Dolos, and Codequiry actually reported on the same cohort.

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General 11 min

Rachel Foster • 1 week ago

How a Lecturer Catches Code Translated Between Languages

MOSS and JPlag compare Java to Java and Python to Python, which means a translated submission can score in single digits while the logic stays identical. This is how one lecturer, a TA, and a department chair handle ports, and what they've learned about the tooling that catches them.

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General 12 min

Rachel Foster • 2 weeks ago

How AST Comparison Catches Refactored Code Plagiarism

Renaming variables and swapping a for loop for a while loop defeats simple text matching, but it rarely defeats structural comparison. This report walks through the obfuscation ladder, the algorithms that climb it, and the published detection rates behind the claims, including the cases where every engine still misses.

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General 8 min

Rachel Foster • 3 weeks ago

The Evolution of Code Similarity Detection in One CS Department

A University of Missouri-St. Louis instructor spent two decades watching code similarity tools change shape. The gaps between each generation of detection explain why instructors now run peer, web, and AI checks side by side.

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General 8 min

Rachel Foster • 4 weeks ago

A TA's Script for Sorting 300 Code Similarity Reports by Office Hours

A teaching assistant at UC San Diego reduced a 312-submission similarity queue to a shortlist of 14 files in about two hours. The workflow relies on Codequiry's outlier scoring, a Python triage script, and a strict two-pass review rule. Here is the exact process, including the script and the thresholds she uses.

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General 11 min

Rachel Foster • 1 month ago

Benchmarking MOSS JPlag and Codequiry on 200 Student Submissions

A public university ran MOSS, JPlag, and Codequiry against the same 214 Python submissions, plus 30 AI-generated files. The tools disagreed on nearly a quarter of flagged cases. One combined approach changed how instructors review code.

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General 8 min

Rachel Foster • 1 month ago

AI Code Detector False Positives on Boilerplate

AI code detectors are producing false positives on the most ordinary submissions in CS1: code that looks the same because the assignment required it. This reported piece examines the data, the workflow changes instructors are making, and why combining AI detection with structural similarity reduces the error rate.

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General 6 min

Rachel Foster • 1 month ago

Automating Code Plagiarism Detection in GitHub Actions With Codequiry

Set up an automated code plagiarism detection pipeline in GitHub Actions using Codequiry's REST API. Follow precise steps to write a workflow YAML and a Python script that submits student code, receives similarity scores, flags suspicious pushes, and optionally detects AI-generated code. Includes threshold tuning, result interpretation, and false positive handling.

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General 7 min

Rachel Foster • 2 months ago

A Hiring Manager's Audit of AI-Generated Code in Take-Home Tests

An engineering leader audit of 1,284 remote take-home coding submissions found 31.2% flagged as likely AI-generated at high confidence. Manual review confirmed 279 of 401 high-confidence flags, with a 4.5% false positive rate among high-confidence flags. Here is what the data showed and how hiring managers should handle AI detection scores.

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General 10 min

Rachel Foster • 2 months ago

How Few AST Nodes Do You Need to Catch a Copied Function

A single function with renamed variables, reordered statements, and changed whitespace can still look structurally identical under the hood. This step-by-step guide builds a minimal AST clone detector in Python, explains where it breaks, and shows how production tools like Codequiry stack structural, token‑level, and web‑origin checks to catch the copying that student‑grade normalizers miss.

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General 7 min

Rachel Foster • 2 months ago

How UMass Amherst Brought AI Detection Into CS 121

When 800 students enroll in an introductory Python course, detecting AI-generated code by hand is impossible. UMass Amherst integrated an AI code detector trained on student-level patterns alongside traditional similarity checks—and uncovered a 14% AI flag rate, a 2% false positive rate, and a sustainable workflow that kept TAs focused on teaching rather than policing.

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