Code Intelligence Hub

General 8 min

James Okafor · 2 months ago

How Code Similarity Detection Advanced From Strings to Semantics

From manual diff checks to AI-powered semantic analysis, code plagiarism detection has undergone a fundamental transformation. This article traces the key milestones—MOSS, JPlag, AST fingerprinting, and the new frontier of LLM-written code—and explains why a single method is no longer enough.

Read →

General 10 min

Alex Petrov · 2 months ago

How Much Copied Stack Overflow Code Do Plagiarism Tools Actually Catch

Traditional similarity tools like MOSS and JPlag compare student submissions against each other but leave a massive blind spot: code copied directly from Stack Overflow, GitHub repositories, and online tutorials. This article examines how web source detection works, what it catches that peer comparison misses, and why both approaches together give you the real picture of code originality.

Read →

AI Detection 9 min

Priya Sharma · 2 months ago

How Burstiness and Perplexity Catch AI-Generated Code

Burstiness and perplexity aren't just linguistic curiosities—they're the primary statistical signals that distinguish human-written source code from LLM output. This article explains exactly how these measures work under the hood, with worked examples, real-world detection rates, and honest limitations.

Read →

Case Studies 2 min

David Kim · 2 months ago

One Community College's Web Code Plagiarism Strategy

When intro programming students at a mid-sized community college were copying entire code snippets from Stack Overflow and GitHub, the department needed a scalable detection solution. By integrating Codequiry’s web-source matching into their grading pipeline, they reduced surface-level copy-paste incidents by 40% in a single semester while cutting manual review time by 60%.

Read →

General 8 min

James Okafor · 2 months ago

Cross-Language Code Plagiarism Detection Methods Tested

A rigorous head-to-head comparison of three cross-language code plagiarism detection approaches—tokenization, AST matching, and semantic fingerprinting—tested on 100 student-style assignments translated between Java, Python, and C++. We reveal which method catches translated loops, renamed variables, and switched control flow, and which one drowns in false positives.

Read →

Tutorials 8 min

Emily Watson · 2 months ago

Automating Code Plagiarism Detection in Your Grading Workflow

A practical walkthrough for CS instructors who want to wire code similarity checks directly into their grading workflow. Covers tooling choices, LMS integration, and how to layer in web-source and AI-generated code detection for a complete academic integrity pipeline.

Read →

General 9 min

Emily Watson · 2 months ago

K-gram Fingerprinting for Source Code Similarity Analysis

K-gram fingerprinting is the backbone of modern code plagiarism detection. This step-by-step guide walks through tokenization, k-gram generation, hashing, winnowing, and comparison — the exact pipeline used by MOSS and Codequiry. Includes Python code examples, algorithmic tradeoffs, and real-world scaling numbers.

Read →

Tutorials 7 min

Alex Petrov · 2 months ago

Automated Code Similarity Checks in a CI Lab Pipeline

Setting up automated code plagiarism and similarity checks inside a CI pipeline cuts manual grading time and catches copying that individual reviewers miss. This practical guide walks through the architecture, tooling choices, and honest tradeoffs of running MOSS, JPlag, or Codequiry’s API on every lab push.

Read →

General 1 min

Emily Watson · 2 months ago

How Abstract Syntax Tree Comparison Detects Restructured Code

Abstract syntax tree (AST) comparison is a powerful technique for detecting code plagiarism that has been restructured through variable renaming, method reordering, and whitespace changes. This article explains how AST comparison works, its strengths and limitations, and when to combine it with token-based methods for best results.

Read →

General 8 min

Dr. Sarah Chen · 2 months ago

Why Some CS Departments Are Moving Beyond Moss for Plagiarism Detection

Riverdale State University’s computer science department spent years relying on Moss to catch plagiarised assignments. But as student work grew more sophisticated — combining copied web code, heavy refactoring, and AI-generated fragments — the department realised token-based similarity alone was no longer sufficient. This case study covers how they transitioned to a multi-tool detection pipeline.

Read →

General 10 min

Marcus Rodriguez · 2 months ago

What Code Fingerprinting Is and How It Catches Plagiarism

Source-code fingerprinting is the core technique behind every major plagiarism detection tool, from MOSS to Codequiry. This guide explains how it works at the algorithm level, shows you how to interpret its output, and offers practical strategies for designing assignments that resist its limitations.

Read →

Case Studies 9 min

Emily Watson · 2 months ago

How One Bootcamp Built a Code Originality Pipeline

When CareerDevs Academy scaled from 30 to 200 students per cohort, their manual code review process couldn't keep up with plagiarism and improper code reuse. Here's how they built a tiered originality pipeline combining static analysis, similarity detection, and educational intervention — and what other programs can learn from their approach.

Read →