Detailed Results - Codequiry API

Detailed Plagiarism Analysis

Get comprehensive, line-by-line plagiarism analysis for specific submissions. This endpoint provides detailed match information, source code comparisons, and forensic-level data for investigating potential plagiarism.

Endpoint

🔍 Detailed Analysis Overview

While the overview endpoint gives you summary scores, the detailed results endpoint provides forensic-level analysis of exactly what code matches, where it matches, and how similar it is. This is essential for investigating suspected plagiarism cases.

Deep Analysis: This endpoint returns source code content, line-by-line matches, and similarity breakdowns for thorough plagiarism investigation.

What You'll Get

Complete Source Code: Full content of analyzed files with parsed structure
Peer Matches: Exact line ranges where code matches other submissions
External Matches: Similarities found in web/database sources
Match Statistics: Average, minimum, and maximum similarity scores
Related Files: Source code of matched submissions for comparison
Match Metadata: Token counts, similarity percentages, and match types

📤 Request Parameters

Specify both the check ID and the specific submission ID you want to analyze in detail.

Parameter	Type	Required	Description
`check_id`	Integer	✅ Yes	ID of the completed check
`submission_id`	Integer	✅ Yes	ID of specific submission to analyze (from overview response)

Request Example

		curl -X POST \
  'https://codequiry.com/api/v1/check/results?check_id=2810&submission_id=14589' \
  -H 'Accept: application/json' \
  -H 'apikey:  YOUR_API_KEY_HERE '
		
			
				cURL Command

Detailed Response

        {
    "submission": {
        "id": 14589,
        "filename": "student3_python_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",
        "submissionfiles": [
            {
                "id": 128042,
                "submission_id": 14589,
                "filedir": "student3_assignment/calculator.py",
                "content": "def calculate_factorial(n):\n    result = 1\n    for i in range(1, n + 1):\n        result *= i\n    return result\n\nprint(calculate_factorial(10))",
                "created_at": null,
                "updated_at": null,
                "language_id": 14
            }
        ]
    },
    "avg": 32.43,
    "max": "85.45",
    "min": "12.50",
    "peer_matches": [
        {
            "id": 616853,
            "submission_id": 14589,
            "submission_id_matched": 14587,
            "similarity": "85.45",
            "matched_similarity": "78.30",
            "file": "student3_assignment/calculator.py",
            "file_matched": "student1_assignment/main.py",
            "line_start": 1,
            "line_end": 5,
            "tokens": 12,
            "created_at": null,
            "updated_at": null,
            "line_matched_start": 3,
            "line_matched_end": 7,
            "match_type": 1
        }
    ],
    "other_matches": [
        {
            "id": 789123,
            "submission_id": 14589,
            "source_type": "web",
            "source_url": "https://stackoverflow.com/questions/12345",
            "similarity": "67.80",
            "file": "student3_assignment/calculator.py",
            "line_start": 1,
            "line_end": 4,
            "tokens": 8,
            "match_snippet": "def calculate_factorial(n):\n    result = 1\n    for i in range(1, n + 1):",
            "match_type": 2
        }
    ],
    "related_submissions": [
        {
            "id": 14587,
            "filename": "student1_assignment",
            "total_result": "78.30"
        }
    ],
    "related_files": [
        {
            "id": 128043,
            "submission_id": 14587,
            "filedir": "student1_assignment/main.py",
            "content": "import math\n\ndef factorial_calc(number):\n    result = 1\n    for i in range(1, number + 1):\n        result *= i\n    return result\n\nprint(factorial_calc(10))",
            "created_at": null,
            "updated_at": null,
            "language_id": 14
        }
    ]
}
        
            
                JSON Response

Testing Note: This endpoint requires valid check_id and submission_id from completed analysis. Use the overview endpoint first to get submission IDs.

📊 Response Structure Explained

Main Response Fields

Field	Type	Description
`submission`	Object	Complete submission data including source files
`avg/max/min`	Number	Statistical summary of similarity scores across all matches
`peer_matches`	Array	Line-by-line matches with other submissions in the same check
`other_matches`	Array	Matches found in external databases and web sources
`related_files`	Array	Source code of matched submissions for side-by-side comparison

Match Object Structure

Field	Description
`similarity`	Similarity percentage for this specific match
`file`	Path to the file within the submission
`line_start/line_end`	Line range of the matched code section
`tokens`	Number of code tokens that matched
`match_type`	Type of match: 1=peer, 2=web, 3=database

🎯 Match Types Explained

Type 1: Peer Matches

Similarities between submissions uploaded to the same check.

Most common for academic assignments
Indicates potential student collaboration
Includes submission_id_matched for reference
Shows exact line ranges in both files

Type 2: Web Matches

Code found on websites, forums, and online repositories.

Common sources: Stack Overflow, GitHub
Includes source_url when available
Shows match_snippet of found code
May indicate copied solutions

Type 3: Database Matches

Similarities with Codequiry's plagiarism database.

Historical submissions from other institutions
Previously detected plagiarism patterns
Academic paper and textbook content
Anonymous source protection

🔍 Code Comparison Visualization

Use the detailed match data to create side-by-side code comparisons:

Original Submission

student3_assignment/calculator.py (lines 1-5)

                        def calculate_factorial(n):
    result = 1
    for i in range(1, n + 1):
        result *= i
    return result
                        
                            
                                85.45% Similar

Matched Submission

student1_assignment/main.py (lines 3-7)

                        def factorial_calc(number):
    result = 1
    for i in range(1, number + 1):
        result *= i
    return result
                        
                            
                                78.30% Similar

💻 Implementation Examples

Processing Match Data

JavaScript Processing

                        // Extract high-similarity matches
const highRiskMatches = data.peer_matches.filter(match => 
    parseFloat(match.similarity) > 70
);

// Group matches by type
const matchesByType = {
    peer: data.peer_matches,
    web: data.other_matches.filter(m => m.source_type === 'web'),
    database: data.other_matches.filter(m => m.source_type === 'database')
};

// Calculate average similarity
const avgSimilarity = data.peer_matches.reduce((sum, match) => 
    sum + parseFloat(match.similarity), 0
) / data.peer_matches.length;
                        
                            
                                JavaScript

Python Analysis

                        import json

def analyze_plagiarism_results(response_data):
    # Parse response
    submission = response_data['submission']
    peer_matches = response_data['peer_matches']
    
    # Identify suspicious patterns
    high_similarity = [
        match for match in peer_matches 
        if float(match['similarity']) > 80
    ]
    
    # Extract matched code segments
    for match in high_similarity:
        start_line = match['line_start']
        end_line = match['line_end']
        file_content = submission['submissionfiles'][0]['content']
        
        # Extract specific lines
        lines = file_content.split('\n')
        matched_code = '\n'.join(lines[start_line-1:end_line])
        
        print(f"Match: {match['similarity']}% similarity")
        print(f"Code: {matched_code}")
                        
                            
                                Python

⚠️ Error Responses

Invalid Submission ID (422 Unprocessable Entity)

        {
    "error": "Invalid submission_id provided"
}
        
            
                Error Response

Submission Not Found

The submission_id doesn't exist in the specified check

Verify the submission ID from the overview response

Check Incomplete

Results not available - analysis still processing

Wait for check to complete (status_id = 4)

🎯 Investigation Best Practices

Focus Areas

Matches with >70% similarity
Large token counts (>20)
Multiple matches per submission
Consecutive line ranges

Code Analysis

Compare variable naming patterns
Check comment similarities
Look for unique code structures
Analyze whitespace patterns

Context Matters

Consider assignment complexity
Account for common patterns
Check template/starter code
Review submission timing

🚀 Next Steps

After analyzing detailed results, you may want to download files or generate reports:

Code Plagiarism Detection API

API Playground