Lineage-Impact Trace

The problem

It is difficult to see how far a single data error will spread through a complex system. Tracking the ripple effect of a mistake across multiple layers of data is often unclear.

What it does

It maps out the path of data and calculates a score that shows how far a specific error can travel. It identifies which parts of a system are most affected by a single point of failure.

Why it matters

It allows you to see exactly how deep a data error will spread so you can prioritize where to fix it.

Validation

It was run inside an isolated container with no network access. This is the exact command and the real output it produced — captured process output, not written by a model.

$ python3 lineage_impact_trace.py
Lineage Propagation Score for UIComponent: 1.52

A screenshot of that run.

A clean run proves this does what is shown above, in a CPU-only sandbox. It is a small research demo — not a production tool, and nothing here was published anywhere.

The code

All of it — 87 lines, one file, standard library only.

# Lineage-Impact Trace Implementation

class Module:
    def __init__(self, name, dependencies=None):
        self.name = name
        self.dependencies = dependencies or []

    def blast_radius(self):
        """
        Structural Impact Trace logic calculating how deep changes propagate
        """
        if not self.dependencies:
            return 1  # Base case: no dependencies
        
        # Calculate weighted impact based on dependency types
        weights = [0.8 if 'data' in dep.name.lower() else 0.5 for dep in self.dependencies]
        return 1 + sum(weights) / len(weights) if weights else 1

    def lineage_trace(self):
        """
        CryoTrack-inspired lineage tracking for data flow
        """
        visited = set()
        stack = [self]
        
        while stack:
            current = stack.pop()
            if current not in visited:
                visited.add(current)
                stack.extend(dep for dep in current.dependencies if dep not in visited)
        
        return visited

    def path_optimized_logic_trace(self):
        """
        Combines multi-point path planning with functional decomposition
        """
        # Simulate critical paths using depth-based traversal
        critical_paths = []
        stack = [(self, [self])]
        
        while stack:
            current, path = stack.pop()
            critical_paths.append(path)
            
            # Prioritize data dependencies first
            data_deps = [dep for dep in current.dependencies if 'data' in dep.name.lower()]
            other_deps = [dep for dep in current.dependencies if dep not in data_deps]
            
            stack.extend((dep, path + [dep]) for dep in data_deps + other_deps)
        
        return critical_paths

    def lineage_propagation_score(self):
        """
        Calculates the combined impact score for data-flow disruptions
        """
        # Get all impacted modules via lineage trace
        impacted_modules = self.lineage_trace()
        
        # Calculate blast radius for each impacted module
        blast_radii = [mod.blast_radius() for mod in impacted_modules]
        
        # Combine with critical paths from path-optimized trace
        critical_paths = self.path_optimized_logic_trace()
        path_weights = [len(path) for path in critical_paths]  # Longer paths have higher weight
        
        # Score is weighted average of blast radii across critical paths
        total_weight = sum(path_weights)
        if total_weight == 0:
            return 0
        
        return sum(r * len([p for p in critical_paths if mod in p]) for mod, r in zip(impacted_modules, blast_radii)) / total_weight


# Example usage
if __name__ == "__main__":
    # Create sample module graph
    data_layer = Module('DataLayer', [])
    api_client = Module('APIClient', [data_layer])
    business_logic = Module('BusinessLogic', [api_client, data_layer])
    ui_component = Module('UIComponent', [business_logic])
    
    # Calculate and print scores
    root_module = ui_component
    score = root_module.lineage_propagation_score()
    print(f"Lineage Propagation Score for {root_module.name}: {score:.2f}")