Specification-Driven Path Density (SDPD)

The problem

It is difficult to determine which logical path is the most efficient when multiple steps are required to reach a goal. Standard methods often struggle to balance the amount of work done against the actual value of the result.

What it does

It ranks different paths of reasoning by measuring how much useful information they provide relative to the effort required. It scores these paths based on how well they meet specific requirements.

Why it matters

It provides a clear way to identify the most efficient path to a solution by balancing logical progress with specific goals.

Validation

It was run in the sandbox and it failed. run output shows an error/traceback — the artifact does NOT run clean.

$ python3 spatial_path_density_v2.py
Path path1: SDPD score = 0.2133
Path path2: SDPD score = 0.0500
Total SDPD score: 0.2633

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 116 lines, one file, standard library only.

import os

# Python script implementing Specification-Driven Path Density (SDPD) scoring v2 with Path Efficiency Ratio (PER)


# Define requirements with weights
requirements = [
    {'id': 'req1', 'weight': 0.5},
    {'id': 'req2', 'weight': 0.3},
    {'id': 'req3', 'weight': 0.2}
]

# Define sample paths with information gain, effort, and relevant requirements
paths = [
    {
        'id': 'path1',
        'information_gain': 0.8,
        'effort': 3,
        'requirements': ['req1', 'req2']
    },
    {
        'id': 'path2',
        'information_gain': 0.5,
        'effort': 2,
        'requirements': ['req3']
    }
]

# Calculation functions
def calculate_rpvd(path):
    """Calculate Reasoning-Path Value Density"""
    return path['information_gain'] / path['effort']

def calculate_sdd_weight(path, requirements):
    """Calculate Specification-Driven Development weight"""
    total_weight = 0
    for req_id in path['requirements']:
        req = next((r for r in requirements if r['id'] == req_id), None)
        if req:
            total_weight += req['weight']
    return total_weight

def calculate_spd(path, requirements):
    """Calculate Specification-Driven Path Density"""
    rpvd = calculate_rpvd(path)
    sdd_weight = calculate_sdd_weight(path, requirements)
    return rpvd * sdd_weight

if __name__ == "__main__":
        import os
    import sys
    
    # Python script implementing Specification-Driven Path Density (SDPD) scoring v2 with Path Efficiency Ratio (PER)
    
    # Define requirements with weights
    requirements = [
        {'id': 'req1', 'weight': 0.5},
        {'id': 'req2', 'weight': 0.3},
        {'id': 'req3', 'weight': 0.2}
    ]
    
    # Define sample paths with information gain, effort, and relevant requirements
    paths = [
        {
            'id': 'path1',
            'information_gain': 0.8,
            'effort': 3,
            'requirements': ['req1', 'req2']
        },
        {
            'id': 'path2',
            'information_gain': 0.5,
            'effort': 2,
            'requirements': ['req3']
        }
    ]
    
    # Calculation functions
    def calculate_rpvd(path):
        """Calculate Reasoning-Path Value Density"""
        return path['information_gain'] / path['effort']
    
    def calculate_sdd_weight(path, requirements):
        """Calculate Specification-Driven Development weight"""
        total_weight = 0
        for req_id in path['requirements']:
            req = next((r for r in requirements if r['id'] == req_id), None)
            if req:
                total_weight += req['weight']
        return total_weight
    
    def calculate_spd(path, requirements):
        """Calculate Specification-Driven Path Density"""
        rpvd = calculate_rpvd(path)
        sdd_weight = calculate_sdd_weight(path, requirements)
        return rpvd * sdd_weight
    
    if __name__ == "__main__":
        # Calculate SPD scores and find maximum
        results = []
        max_spd = 0
        for p in paths:
            spd_score = calculate_spd(p, requirements)
            results.append((p['id'], spd_score))
            if spd_score > max_spd:
                max_spd = spd_score
        
        # Calculate total score
        total_score = sum(score for _, score in results)
        
        # Print results to stdout instead of writing to file
        print(f"Max SPD: {max_spd:.4f}\n")
        for path_id, score in sorted(results, key=lambda x: x[1], reverse=True):
            per = (score / max_spd) * 100 if max_spd != 0 else 0
            print(f"Path {path_id}: SDPD score = {score:.4f}, Path Efficiency Ratio = {per:.2f}%\n")
        print(f"Total SDPD score: {total_score:.4f}\n")