Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
11 min
Marcus Rodriguez
·
1 month ago
Most statements of work say "original work" and never define it, which is how GPL code ends up in your settlement service. Here is the four-question intake review I run on every contractor deliverable, with the thresholds and tooling that hold up under scrutiny.
General
12 min
Rachel Foster
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1 month ago
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.
General
10 min
James Okafor
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1 month ago
Twenty-three of 412 submissions in a data structures course shared a Dijkstra implementation that differed by fewer than three tokens, and the peer similarity engine flagged all 23 as a single cluster. Nobody had copied anybody. Here is the mechanism behind convergent AI output, the identifiers and AST evidence that separated it from real collusion, and the three-pass workflow we used to keep the scores from contaminating each other.
General
11 min
James Okafor
·
1 month ago
Cross-language code plagiarism detection compares normalized structure rather than raw text, which works when a translation was mechanical and fails when the student rewrote the algorithm. Here is what survives a Java-to-Python translation, what the token and IR approaches actually see, and how to run the check across a whole cohort without drowning in false positives.
General
13 min
Alex Petrov
·
1 month ago
A Java submission and a Python submission looked nothing alike, but they were the same algorithm translated line by line. This is the story of how cross-language code plagiarism detection actually works, where it catches translated code, and where it still fails.
General
9 min
Priya Sharma
·
1 month ago
Similarity scores are ranking signals, not verdicts. I'll walk through the distributions, thresholds, and triage rules I use when reviewing code similarity reports for 400-student courses, plus where AI-generated code fits in the same queue.
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
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
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
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2 months 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
·
2 months 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
·
2 months 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.