Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
8 min
Dr. Sarah Chen
·
3 weeks ago
A CS professor traces how AI-generated code detection grew out of MOSS-era token fingerprints, code stylometry, and a broken similarity assumption. The piece explains how modern detectors work, where they still stumble, and why stacked peer, web, and AI signals make the most defensible academic workflow.
General
11 min
Rachel Foster
·
3 weeks ago
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.
General
11 min
David Kim
·
3 weeks ago
A DevSecOps lead's field notes on running AI-generated code detection as a merge request gate in GitLab CI, with the exact YAML, thresholds, and audit trail decisions that survived a fintech rollout.
General
11 min
Dr. Sarah Chen
·
4 weeks ago
One CS department's switch from a single similarity tool to a layered detection workflow changed what they could see in student code. Peer copying, web sources, and AI-generated submissions each required different signals, and combining them revealed more than any one check alone.
General
8 min
David Kim
·
1 month ago
Most vendor code passes unit tests and static analysis. It fails provenance checks. This is the exact merge gate I run in a fintech CI pipeline: Codequiry's plagiarism and AI scans wired into GitHub Actions, with thresholds, evidence review, and a failure policy.
General
10 min
James Okafor
·
1 month ago
A practical, code-level guide to batch-scanning Python files for web-sourced code. We walk through token normalization, fingerprinting, uploading to Codequiry, interpreting web match URLs, and stacking an AI check on flagged files. Built for CS professors auditing assignments and engineering managers verifying contractor code.
General
10 min
Dr. Sarah Chen
·
1 month ago
A CS professor ran 1,200 Java submissions through three AI code detectors. Codequiry caught 94% of known AI files and flagged only 3.5% of pre-LLM human code, while the other tools posted two to three times that false positive rate. The full numbers and methods are below.
General
9 min
Priya Sharma
·
1 month ago
Across 41,000 student submissions at a large public university, an 85% token-level similarity score between two students predicted confirmed misconduct 92% of the time in introductory courses. This guide walks through the exact calibration workflow, score distributions by language, and tiered review thresholds that worked for my assessment team.
General
11 min
Marcus Rodriguez
·
1 month ago
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.
General
14 min
Emily Watson
·
1 month ago
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.
General
10 min
Alex Petrov
·
1 month ago
Quoted Google searches used to be the standard way to catch a Stack Overflow lift. I tracked the shift across tools, fingerprints, and live web corpora, and why modern checks need to pair source matching with AI detection. The short version: the web changed the question from "who copied whom" to "where did this code come from."
General
11 min
Emily Watson
·
1 month ago
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.