ucm-reason

The reasoning brain of Unified Context Management (UCM) — impact analysis, test intent, and explanation chains.

ucm-reason answers the questions that make a knowledge graph actionable:

1. What changed?
2. What is impacted? — reverse BFS over the dependency graph, decaying confidence at each hop
3. What is not impacted? — and why (equally important for scoping)
4. What should be tested? — test intent grouped into confidence tiers
5. What is uncertain? — ambiguity detection (source conflicts, stale data, requirement drift)
6. Why? — every output carries an ExplanationChain of ReasoningSteps (evidence → inference → confidence)

What it does

• impact — analyze_impact() runs a reverse breadth-first search from the changed entities, attenuating confidence by graph distance, and classifies results into direct/indirect impacts plus an explicit not impacted set.
• intent — generate_test_intent() turns an impact report into must-test / should-test tiers, key risks, coverage gaps, and "decided not to test" rationale.
• explanation — human-readable derivation chains (to_narrative()).
• ambiguity — flags low-confidence edges, stale data, and source conflicts.

Quick example

use ucm_graph_core::{UcmGraph, EntityId};
use ucm_reason::{analyze_impact, generate_test_intent};

fn report(graph: &UcmGraph, changed: &[EntityId]) {
    // analyze_impact(graph, changed_entities, min_confidence, max_depth)
    let impact = analyze_impact(graph, changed, 0.3, 10);
    let intent = generate_test_intent(&impact);

    println!("direct impacts: {}", impact.direct_impacts.len());
    println!("must-test scenarios: {}", intent.high_confidence.len());
}

Why these models

Research foundations

• Change awareness — Meta Predictive Test Selection (ICSE-SEIP 2019): impact attenuates with dependency-graph distance; 33% of tests caught 99.9% of failures.
• Scale of impact — Google TAP: beyond ~10 hops, testing approaches a 99:1 noise-to-signal ratio.

License

MIT — see LICENSE.