Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
6 min
Alex Petrov
·
1 month ago
A hands-on retrospective on how code similarity detection grew from naive line diffs to tokenization, ASTs, and fingerprinting. Follow a step-by-step Python prototype and a production workflow with Codequiry to catch refactored plagiarism in CS courses.
General
8 min
Dr. Sarah Chen
·
1 month 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
8 min
Rachel Foster
·
1 month ago
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.
General
7 min
David Kim
·
1 month ago
License scanning went from hand-checking COPYING files to SPDX metadata and policy-as-code gates. This piece traces that arc, shows which gaps still let copied code slip through, and explains how to pair license scans with source matching for audit-grade evidence.
General
11 min
Rachel Foster
·
1 month 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
·
1 month 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
9 min
Marcus Rodriguez
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1 month ago
A student renames every variable and converts for loops to while loops. MOSS still flags 94%. This guide builds a minimal winnowing detector in Python 3.11 so you can see exactly why code plagiarism detection algorithms survive refactoring. We then look at where the approach breaks and how AST matching fills the gap.
General
11 min
Dr. Sarah Chen
·
1 month 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
9 min
Alex Petrov
·
1 month ago
A bootcamp instructor walks through the exact workflow he uses to catch student code copied from tutorials, GitHub repos, and Stack Overflow answers, including what peer-only checkers miss, how to read web match reports without false positives, and where Codequiry fits.
General
10 min
James Okafor
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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.