Rachel Foster

General 8 min

Rachel Foster • 4 days 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 week 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 weeks 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 • 3 weeks 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 • 4 weeks 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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General 7 min

Rachel Foster • 1 month ago

Across Two Semesters, AI Code Detector Accuracy Hit 87% in Python

A two-semester experiment at a mid-sized CS department put Codequiry’s AI code detector to the test across 1,200 student submissions. The tool achieved 87% overall accuracy in identifying AI-generated Python code, with a manageable false-positive rate and strong recall. The study surfaced distinct patterns in where detection excels—and where manual judgment remains essential.

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General 9 min

Rachel Foster • 1 month ago

How Code Fingerprints Catch GPL License Violations

Detecting GPL license violations in a codebase requires more than grep. Code fingerprinting and AST-based similarity analysis can identify copied open-source code even after heavy modification. This article explains the techniques behind automated license compliance detection and how enterprises use them to avoid lawsuits.

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General 8 min

Rachel Foster • 1 month ago

Inside a Startup’s Codebase License Compliance Audit

When a fintech startup faced a pre-funding due diligence request, they discovered copies of GPL-licensed libraries scattered across their proprietary codebase. This case study follows their eight-week audit, blending automated scanning tools like Codequiry and FOSSology with manual triage to achieve compliance without derailing their development roadmap.

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General 10 min

Rachel Foster • 3 months ago

How Cross-Language Code Plagiarism Detection Actually Works

Cross-language code plagiarism presents a growing challenge for programming educators as students discover they can translate solutions between languages to evade detection. This article explains the techniques—AST normalization, semantic fingerprinting, and intermediate representation comparison—that modern tools use to catch these sophisticated cases.

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General 9 min

Rachel Foster • 3 months ago

From Paper Traces to Abstract Syntax Trees: Code Similarity Then and Now

The history of code similarity detection is a story of escalating arms races. What started with professors reading printouts has evolved through Unix diffs, token-based fingerprinting, and into modern abstract syntax tree analysis. This retrospective traces the key technical shifts that shaped how we detect code plagiarism in programming courses today.

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General 10 min

Rachel Foster • 4 months ago

Why More CS Departments Are Adopting Layered Detection

Computer science departments are discovering that no single detection method catches every kind of code plagiarism. This article explores the layered detection approach combining structural, web-source, and AI analysis to create a comprehensive academic integrity system.

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General 9 min

Rachel Foster • 4 months ago

What Code Complexity Metrics Miss About Real Maintainability

Cyclomatic complexity, lines of code, and other traditional metrics have been the gold standard for decades — but they systematically miss the factors that actually make code hard to maintain. Here is what experienced teams have learned about measuring what matters.

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