Documentation

Code Integrity
Platform for Education

Detect plagiarism and AI-generated code across 51+ programming languages. Enterprise-grade detection with side-by-side comparison and real-time analysis.

51+
Programming Languages
3
Detection Engines
<2min
Average Analysis Time

Quick Start Guide

Get your first plagiarism check running in under 5 minutes

1

Create Account

Sign up with your educational email. Complete educator verification to unlock all features. No credit card needed to start.

2

Create a Course

Organize your work by creating courses (e.g., "CS101 Fall 2025"). Each course contains multiple checks for different assignments.

3

Create a Check

Inside your course, create a check. Select the programming language and enable your preferred detection methods (peer, web, or AI).

4

Upload & Analyze

Upload student submissions via drag-and-drop, ZIP files, or Desktop App. Click "Run Check" to start analysis and get results in minutes.

Platform Overview

Understanding the key concepts and structure of Codequiry

Courses

Courses are top-level containers for organizing your checks. Create one per class, semester, or project.

  • Unlimited checks per course
  • Enable Student Portal for direct submissions
  • Custom branding options
  • Share with team members

Checks (Assignments)

Each check contains student submissions and runs plagiarism analysis. Configure detection settings per check.

  • Multiple detection engines
  • AI detection toggle
  • Set due dates for submissions
  • Export PDF reports

Submissions

Each submission represents one student's work. Upload individual files, ZIPs, or entire project folders.

  • Drag-and-drop upload
  • Auto-extracts ZIP files
  • Supports nested directories
  • View, download, delete anytime

Detection Methods

Three powerful engines to catch plagiarism and AI-generated code

Peer Comparison

Group Similarity

Compares all submissions within your check against each other. Perfect for detecting collaboration, copying between classmates, or shared solutions.

All-against-all comparison
Fast results (1-3 minutes)
Identifies collaboration clusters
Requires 2+ submissions
Web Sources

Web Check

Checks against billions of online sources including GitHub, Stack Overflow, code forums, and public repositories to find external copying.

GitHub repositories
Stack Overflow & forums
Open-source projects
Links to original sources
AI Detection

AI-Generated Code

Detects code written by AI tools like ChatGPT, GitHub Copilot, Claude, and others. Identifies AI patterns and provides confidence scores.

ChatGPT & Copilot detection
Per-file AI probability
Confidence scoring
Enable/disable per check

Upload Methods

Multiple ways to get student submissions into Codequiry

Web Upload

Upload files directly through the web dashboard with drag-and-drop or file browser.

1 Navigate to your check
2 Drag files or click to browse
3 Upload ZIPs or individual files

Desktop App

Sync local folders automatically. Perfect for LMS downloads or shared drives.

1 Download & install Desktop App
2 Link a local folder to a check
3 Files auto-upload when added

Student Portal

Students submit directly to Codequiry—no account needed. Bypass your LMS.

1 Enable Student Portal on course
2 Share link with students
3 Students upload their ZIP files
Download Desktop App v1.0.2

Reading Results

How to interpret similarity scores and take appropriate action

Similarity Score Ranges

0–20% — Low Similarity
Typically original work. Common patterns like imports or boilerplate may appear.
21–50% — Review Required
May indicate shared starter code or common solutions. Review matched sections.
51–80% — High Similarity
Significant overlap detected. Investigate matched files and document findings.
81–100% — Critical
Extensive copying likely. Review side-by-side comparison and generate PDF report.

Best Practices

Always Review Context

Never judge by percentage alone. Review the actual matched code to understand if it's legitimate reuse.

Consider Starter Code

If you provided template code, expect some similarity. Focus on student-written sections.

Check Collaboration Clusters

The Results Overview shows groups of students with high mutual similarity—helpful for identifying rings.

Generate PDF Reports

Export detailed reports for documentation, academic review boards, or student conferences.

Submit Assignment
Student Name
Student Email
Student ID (optional)
Upload ZIP file...

Student Submission Portal

Let students submit code directly to Codequiry—no accounts required. Share a simple link and collect submissions automatically.

Shareable Links

Get unique URLs per course or per assignment. Share in your LMS, syllabus, or email.

Due Date Enforcement

Set deadlines on assignments. Portal shows countdown and can block late submissions.

Custom Branding

Add your institution's logo and colors. White-label the portal for a professional look.

Email Confirmations

Students receive confirmation emails with submission details and timestamps.

Supported Languages

Full support for 51+ programming languages

Python Java JavaScript C++ C# PHP TypeScript Swift Kotlin Go Ruby Rust MATLAB R SQL +36 more

Frequently Asked Questions

Quick answers to common questions

Navigate to your course dashboard, select the assignment, and click on any submission to view its detailed plagiarism report.
Codequiry supports most programming languages including Python, Java, C++, JavaScript, PHP, and many more. We also support plain text and PDF files.
Most scans complete within 2-5 minutes depending on file size and the number of submissions. Large batches may take longer.
Yes! You can download individual reports as PDF or export batch results as CSV from the assignment page.
Codequiry uses advanced algorithms to compare code structure, logic patterns, and syntax beyond simple text matching.
Essential includes unlimited scans, priority support, API access, advanced reporting, and team collaboration features.
Go to Course Settings > Students, and either invite them via email or share the course enrollment code.
Yes! We support Canvas, Moodle, Blackboard and other LMS platforms. Contact support for integration assistance.
Upload one or more ZIP files containing your source code by dragging and dropping them into the upload area or clicking to browse. Each file can be up to 10MB.
Only ZIP archives are accepted. Your ZIP files can contain source code in any popular programming language including Java, Python, C/C++, JavaScript, C#, PHP, Ruby, Go, Rust, and more.
If your project exceeds 10MB, split it into multiple ZIP files or create separate checks. To reduce file size, exclude build artifacts, dependency folders like node_modules, and version control directories like .git.
Our system compares submissions against each other (peer comparison), our database of past submissions, and optionally against online sources. We use advanced algorithms that detect similarities even when code has been renamed or restructured.
The similarity percentage indicates how much of the submission matches other sources. Higher percentages suggest more overlap. However, some similarity is normal (e.g., common code patterns), so always review the highlighted matches.
Your code is encrypted and secured. We do not store or match your uploaded code in the Codequiry database. Our checks compare against external sources only. Professional plans have additional privacy settings.
Our AI written code detection achieves 80-90%+ accuracy when detecting AI-generated code through multi-layered neural networks trained on millions of code samples. We prioritize false positive reduction, preferring to miss AI-written code rather than incorrectly flag human-written code. When we flag code as AI-generated, it's based on concrete indicators and distinctive patterns that separate machine-generated code from human coding styles.
We can detect AI written code from ChatGPT (GPT-4o, GPT-5, GPT-5.1), GitHub Copilot, Claude (3.5, 4, Opus), Grok, Google Gemini, Cursor AI, Amazon CodeWhisperer, Meta Llama, and all major AI coding assistants. Our General AI Detection model catches any AI-generated code patterns.
We support 65+ programming languages including Python, Java, JavaScript, C++, PHP, Ruby, Go, Rust, Swift, and more.
While no AI detection system is 100% foolproof, our multi-layer fingerprint verification makes it extremely difficult to bypass. We use proprietary pattern recognition trained on LLM writing signatures that go beyond simple text analysis. Students who heavily modify AI-generated code may reduce detection confidence, but distinctive AI patterns often remain. We continuously update our models to detect new evasion techniques.
We prioritize false positive reduction, preferring to miss AI-written code rather than incorrectly flag human-written code. When we flag code as AI-generated, it's based on concrete indicators and distinctive patterns that separate machine-generated code from human coding styles.
AI detection typically takes 10-30 seconds per submission, depending on the code size and complexity. Results are generated in real-time and displayed alongside your plagiarism detection results in a unified report.
Code submissions are processed securely and can be stored in your account for your records and comparison purposes. We never share your code with third parties or use it to train external AI models. You maintain full control over your data and can delete submissions at any time.

Professional Code Integrity

Trusted by educational institutions worldwide to maintain academic integrity in programming courses.