Verdict-Driven Traceability Matrix

The problem

It is difficult to determine how much to trust a specific conclusion when you cannot see the clear path of evidence leading to it.

What it does

It takes a set of claims and a sequence of actions to produce a confidence score based on the available evidence. It maps out how each step contributes to the final result.

Why it matters

It provides a clear way to measure and see the reliability of a conclusion by tracking the evidence behind it.

Validation

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

$ python3 process.py
Traceback (most recent call last):
  File "/work/process.py", line 24, in <module>
    with open('result.json', 'w') as f:
         ^^^^^^^^^^^^^^^^^^^^^^^^
OSError: [Errno 30] Read-only file system: 'result.json'

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 — 25 lines, one file, standard library only.

#!/usr/bin/env python3
import json
import sys

with open('input.json') as f:
    data = json.load(f)

# Calculate score based on evidence weights
score = sum(claim['weight'] for claim in data['evidence'])

# Validate execution path
valid_path = all(step in data['execution_path'] for step in ['login', 'process_input', 'validate', 'output_result'])

result = {
    'status': 'success' if valid_path and score > 0.7 else 'error',
    'score_calculated': score,
    'dimensions': {
        'evidence_weight': score,
        'path_coverage': len([step for step in data['execution_path'] if step in ['login', 'process_input', 'validate', 'output_result']]) / len(data['execution_path']) if data['execution_path'] else 0.5,
        'requirement_coverage': 0.8  # Example placeholder for additional dimension
    }
}

with open('result.json', 'w') as f:
    json.dump(result, f)