Context

In safety- and compliance-heavy domains like medical devices (FDA 21 CFR Part 820) and automotive (ISO 26262), teams often adopt the V-Model to ensure every requirement is implemented and verified. At scale (e.g., a platform with 10k+ requirements, 50k tests, and multiple releases), the core engineering risk isn’t writing code—it’s losing traceability: requirements that aren’t implemented, tests that don’t verify anything, or circular dependencies that make audits fail.

Core Question

Explain how you would represent and validate V-Model traceability using core CS concepts.

1. Modeling: How would you model relationships like Requirement -> Design -> Code -> Test as a data structure? When would you choose a graph vs. a tree?
2. Validation algorithms: Given the traceability structure, how would you:
   • Detect missing coverage (e.g., requirements with no verifying tests)?
   • Detect cycles (e.g., design artifacts that depend on each other in a loop)?
   • Compute impact analysis (e.g., if a requirement changes, which tests must be rerun)?
3. Trade-offs: Discuss time/space complexity, and how you’d handle scale (incremental updates, caching, or precomputation).

Scope Guidance

Answer at a Staff+ depth: define the data model, walk through the algorithms (DFS/BFS, topological sorting, reachability), call out edge cases (many-to-many links, orphan nodes, versioned artifacts), and describe how you’d make this robust in a real engineering workflow (CI gates, audit reports, and change impact).