System-level impact analysis for microservice CI/CD via cross-repository dependency graphs
K11tech Microservice QA System · K11 Software Solutions LLC, Texas, United States · Paper 3
100% P/R/F1 on 74 real PRsTransitive consumer detectionLangGraph · 14 agentsZero false positives
A REST endpoint change passes all local CI checks — yet silently breaks 3 direct and 2 transitive consumers in other repositories. Conventional gates are blind to cross-repo dependencies.
A PR opens in order-service. The LangGraph pipeline runs 4 phases — watch the execution log stream in real time.
k11tech-microservice-qa — PR #482 · order-service
$ k11tech-qa run --pr 482 --repo order-service
Animated dependency graph — nodes and edges render in sequence, revealing the blast radius across depth 1 and depth 2 consumers.
Step through the breadth-first search the system performs to discover all consumers at every depth.
unvisited in queue direct (depth 1) transitive (depth 2) complete
Queue:empty — click Start BFS
Phase A — controlled (15 scenarios)
Precision—
Recall—
F1 score—
Tiers testedB1 / B2 / B3
Phase B — OSS (59 external PRs)
Precision—
Recall—
F1 score—
False positives—
B3 — transitive consumer validation
Chains tested—
Max depth—
Detection rate—
Impact decay0.4× per hop
K11tech Agentic AI QA series
1
Single-repo QA pipeline
LangGraph · 14 agents · Risk-proportionate HITL
2
Detect–Fix–Learn loop
Consensus gate · Auto-remediation
3
Cross-repo impact analysis
This paper · Contract registry · Graph traversal
4
Uncertainty quantification
Conformal prediction · HITL confidence
5
Uncertainty source classification
DATA vs SCOPE · Type-stratified thresholds
Open source · Apache License Version 2.0 · deployable alongside the single-repo QA pipeline