Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
11 min
Rachel Foster
·
1 month ago
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.
General
15 min
Alex Petrov
·
1 month ago
Two Python submissions scored 4% against each other and in the 70s against a Java gist from 2017. Cross-language plagiarism is the fastest-growing blind spot in academic integrity because translation destroys the text while preserving everything that matters. Here's what survives a translation, what detectors actually see, and where the false positives come from.
General
11 min
Marcus Rodriguez
·
1 month ago
Ottenstein's 1976 detector hashed student Fortran token streams, and most of what we run today is a refined version of the same idea. This is the fifty-year arc from line diffs to winnowing, AST matching, web crawling, and statistical AI detection, plus the failure mode that still bites: a 0% similarity score that tells you nothing about authorship.
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
10 min
David Kim
·
1 month ago
Line diffing under-reports copied code and over-reports similar-looking code. Here's what token normalization and AST fingerprinting actually compare, where each one breaks, and how to wire both into a CI pipeline or an academic submission workflow.
General
12 min
Rachel Foster
·
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
11 min
James Okafor
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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
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
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
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
8 min
David Kim
·
2 months 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.