Code Plagiarism
Detection API

📊 Overview Features

The overview endpoint gives you a bird's-eye view of your plagiarism check results, perfect for dashboards, reports, and quick analysis. It focuses on peer-to-peer comparisons between your uploaded submissions.

What You'll Get

• Summary Dashboard Data: Ready-to-use data for charts and visualizations
• Submission List: All uploaded files with their highest similarity scores
• Peer Comparisons: Detailed comparison matrix between submissions
• Chart Data: Pre-formatted data for bar charts and graphs
• Dashboard Link: Direct URL to view results in Codequiry dashboard

📤 Request Parameters

Simple request requiring only your completed check ID to retrieve the overview data.

Request Examples

                curl -X POST \
  'https://codequiry.com/api/v1/check/overview?check_id=2810' \
  -H 'Accept: application/json' \
  -H 'apikey:  YOUR_API_KEY_HERE '
                

                    

                        JSON Format
                    
                
            

                curl -X POST \
  'https://codequiry.com/api/v1/check/overviewCSV?check_id=2810' \
  -H 'Accept: text/csv' \
  -H 'apikey:  YOUR_API_KEY_HERE '
                

                    

                        CSV Format
                    
                
            

JSON Response

        {
    "overviewURL": "http://dashboard.codequiry.com/course/api/assignment/2810/overview",
    "submissions": [
        {
            "id": 14587,
            "filename": "student1_assignment",
            "status_id": 4,
            "created_at": "2024-01-15 14:35:22",
            "updated_at": "2024-01-15 14:45:18",
            "result1": "12.50",
            "result2": "8.75", 
            "result3": "15.20",
            "total_result": "85.45",
            "submissionresults": [
                {
                    "id": 463587,
                    "submission_id": 14587,
                    "submission_id_compared": 14588,
                    "score": "23.80",
                    "created_at": null,
                    "updated_at": null
                },
                {
                    "id": 463588,
                    "submission_id": 14587,
                    "submission_id_compared": 14589,
                    "score": "85.45",
                    "created_at": null,
                    "updated_at": null
                }
            ]
        },
        {
            "id": 14588,
            "filename": "student2_assignment",
            "status_id": 4,
            "created_at": "2024-01-15 14:35:22",
            "updated_at": "2024-01-15 14:45:18",
            "result1": "5.25",
            "result2": "3.80",
            "result3": "7.10", 
            "total_result": "23.80",
            "submissionresults": [
                {
                    "id": 463589,
                    "submission_id": 14588,
                    "submission_id_compared": 14589,
                    "score": "12.30",
                    "created_at": null,
                    "updated_at": null
                }
            ]
        }
    ],
    "bardata": [
        {
            "submission": "student1_assignment",
            "score": "85.45"
        },
        {
            "submission": "student2_assignment", 
            "score": "23.80"
        }
    ]
}
        

            

                JSON Response
            
        
    

📋 Response Data Structure

Main Response Fields

Submission Object Fields

🎯 Understanding Similarity Scores

📈 Data Visualization Examples

Using the bardata for Charts

The bardata array is perfectly formatted for creating visualizations:

                        // Extract labels and data from bardata
const labels = data.bardata.map(item => item.submission);
const scores = data.bardata.map(item => parseFloat(item.score));

// Create bar chart
new Chart(ctx, {
    type: 'bar',
    data: {
        labels: labels,
        datasets: [{
            label: 'Similarity Score (%)',
            data: scores,
            backgroundColor: scores.map(score => 
                score > 70 ? '#dc3545' : 
                score > 30 ? '#ffc107' : '#28a745'
            )
        }]
    }
});
                        

                            

                                Chart.js
                            
                        
                    

                        import matplotlib.pyplot as plt

# Extract data from API response
submissions = [item['submission'] for item in bardata]
scores = [float(item['score']) for item in bardata]

# Create color-coded bar chart
colors = ['red' if score > 70 else 'orange' if score > 30 else 'green' for score in scores]

plt.figure(figsize=(10, 6))
plt.bar(submissions, scores, color=colors)
plt.ylabel('Similarity Score (%)')
plt.title('Plagiarism Detection Results')
plt.xticks(rotation=45)
plt.show()
                        

                            

                                Python
                            
                        
                    

📊 Data Analysis Examples

The JSON response provides rich data that can be processed for various analytical purposes:

JavaScript Analysis Example

        // Analyze overview data for insights
function analyzeResults(overview) {
    const submissions = overview.submissions;
    
    // Calculate risk distribution
    const riskDistribution = {
        high: submissions.filter(s => parseFloat(s.total_result) > 70).length,
        medium: submissions.filter(s => parseFloat(s.total_result) > 30 && parseFloat(s.total_result) <= 70).length,
        low: submissions.filter(s => parseFloat(s.total_result) <= 30).length
    };
    
    // Find most similar pair
    let maxSimilarity = 0;
    let similarPair = null;
    
    overview.bardata.forEach(comparison => {
        if (comparison.similarity > maxSimilarity) {
            maxSimilarity = comparison.similarity;
            similarPair = comparison;
        }
    });
    
    return { riskDistribution, similarPair, maxSimilarity };
}
        

            

                Analysis Function
            
        
    

⚠️ Error Responses & Special Cases

Check Not Completed (422 Unprocessable Entity)

        {
    "error": "The check has not been completed."
}
        

            

                Error Response
            
        
    

Special Score Values

💡 Best Practices

🚀 Next Steps

With overview data in hand, dive deeper into specific plagiarism cases: