Documentation
Comprehensive guide to using the KaguaAI platform
Introduction
KaguaAI is an advanced code similarity and plagiarism detection platform designed to help educators, developers, and organizations maintain code integrity and originality. The platform uses sophisticated algorithms to analyze code submissions and identify potential instances of plagiarism or code reuse.
Note
Getting Started
Using KaguaAI is straightforward. Follow these steps to begin analyzing code for similarities:
- Navigate to the Analyze page
- Select the programming language from the dropdown menu
- Upload your file or paste your code in the provided text area
- Click the "Analyze Code" button
- Review the analysis results in the Results tab
If you want to see KaguaAI in action without uploading your own code, you can use our sample files:
- Go to the Analyze page
- Click on "Load Sample Code" below the text areas
- Click "Analyze Code" to see the results
Features
KaguaAI offers a comprehensive set of features designed to provide thorough code analysis:
Analyze code in Java, Python, JavaScript, C++, C#, and more programming languages.
Identify similarities even when code has been refactored or variable names changed.
Pinpoint exactly where similarities occur with line-by-line highlighting.
Get recommendations on how to address potential plagiarism issues.
Code Analysis
KaguaAI employs several techniques to analyze code for similarities:
Tokenization
Tokenization breaks down code into its fundamental components (tokens), such as keywords, identifiers, operators, and literals. This process allows KaguaAI to analyze the structural elements of code rather than just the text.
By comparing token sequences, KaguaAI can identify similarities in code structure even when superficial elements like variable names or formatting have been changed.
Understanding Results
After analyzing your code, KaguaAI provides a detailed report with several key components:
Similarity Score
The overall percentage of similarity between the analyzed code samples. This score ranges from 0% (completely different) to 100% (identical). The score is color-coded for quick assessment:
- Green (0-30%): Low similarity, likely coincidental
- Yellow (31-60%): Moderate similarity, may warrant investigation
- Red (61-100%): High similarity, strong indication of code copying
Matched Sections
Specific sections of code that show similarities. Each match includes:
- Line numbers in both code samples
- Match type (e.g., Exact Match, Similar Structure, Variable Renamed)
- Confidence level for the match
- Source information when available (e.g., GitHub repository, StackOverflow post)
Highlighted Code
Visual representation of the code with similar sections highlighted. This allows for easy identification of specific areas of concern. The comparison view shows:
- Your submitted code with highlighted matches
- The potential source of similar code with corresponding highlights
- Line numbers for easy reference
- Links to original sources when available
Recommendations
Actionable suggestions based on the analysis results. These may include:
- Steps to address potential plagiarism
- Considerations for determining whether similarities are problematic
- Suggestions for code improvements or refactoring
- Language-specific recommendations for alternative implementations
Supported Languages
KaguaAI currently supports the following programming languages:
Support for additional languages is continuously being added. If you need analysis for a language not listed here, please contact us.
Use Cases
KaguaAI serves diverse needs across different sectors:
Professors and teaching assistants use KaguaAI to ensure academic integrity in programming assignments and exams. The platform helps identify unauthorized collaboration or code copying, while also providing identify unauthorized collaboration or code copying, while also providing valuable feedback to students.
Organizers of hackathons and coding competitions use KaguaAI to verify the originality of submissions and ensure fair evaluation.
Development teams use KaguaAI to ensure compliance with licensing requirements and avoid intellectual property issues when incorporating third-party code.
Maintainers use KaguaAI to verify that contributions don't include code with incompatible licenses or unauthorized copies from other projects.
How KaguaAI Works
KaguaAI employs a multi-layered approach to code similarity detection:
- Preprocessing: Code is normalized by removing comments, standardizing whitespace, and converting to a common format.
- Tokenization: The code is broken down into tokens (keywords, identifiers, operators, etc.) to focus on structural elements rather than superficial formatting.
- Similarity Analysis: Multiple algorithms, including Levenshtein distance, token-based comparison, and abstract syntax tree analysis, are applied to identify similarities.
- Result Compilation: The findings are aggregated into a comprehensive report with an overall similarity score, highlighted matches, and actionable recommendations.
This approach allows KaguaAI to detect similarities even when code has been significantly altered through variable renaming, restructuring, or other obfuscation techniques.
Frequently Asked Questions
How accurate is KaguaAI's similarity detection?
KaguaAI's algorithms are highly accurate in detecting code similarities, but the interpretation of these similarities requires human judgment. The platform provides a confidence level for each match to help users assess the reliability of the detection.
Can KaguaAI detect plagiarism if variable names have been changed?
Yes, KaguaAI uses tokenization and normalization techniques to identify structural similarities in code, even when variable names, formatting, or other superficial elements have been changed.
Is my code secure when I upload it to KaguaAI?
Yes, all code uploaded to KaguaAI is encrypted in transit and at rest. We do not store your code after the analysis is complete unless you explicitly opt to save it. For more information, please refer to our Privacy Policy.
How does KaguaAI handle code that uses common libraries or standard algorithms?
KaguaAI is designed to recognize common programming patterns and standard implementations of algorithms. The platform provides context for detected similarities, allowing users to distinguish between legitimate use of standard code and potential plagiarism.