Coverage Engineering
Coverage Engineering is the capability for designing what a specialist AI benchmark must represent. It turns requirements, project knowledge, existing cases, and observed failures into a structured coverage map that guides dataset selection, case construction, expert contributions, and evaluation interpretation.
Definition
Coverage work has two scopes. At project scope, Coverage Facets manages reusable Dimensions, Project Topics, and Case Construction Patterns. At benchmark scope, Coverage Management applies those facets to setup, representation, Coverage Stories, Case Review, Case Foundry, and contribution requests. Benchmark Datasets owns the selected Cases and snapshots that embody the resulting coverage.
Coverage Engineering is therefore broader than adding cases. It explains why a behavior slice matters, how it is represented, which combinations are thin, and what work should close the gap.
Decision checkpoint
| Question | Product surface | Durable result |
|---|---|---|
| Which axes distinguish important behavior? | Coverage Facets → Dimensions | Reusable coverage axes |
| Which domain subjects must be represented? | Coverage Facets → Project Topics | Project topic structure |
| How should cases be constructed repeatedly? | Coverage Facets → Case Construction Patterns | Reusable construction guidance |
| What should this benchmark cover? | Coverage Management → Get Started | Benchmark-specific coverage guidance |
| Where is the selected dataset thin? | Representation and Coverage Stories | Named gaps and sourcing work |
| Which exact cases define evidence? | Benchmark Datasets | Selection and snapshot boundary |
Coverage map and benchmark evidence
A coverage map should identify meaningful combinations rather than isolated tags. A source-freshness Dimension may be well populated overall while the combination of superseded source, high customer impact, and exception request remains absent. Coverage Stories make those combinations operational; Case Foundry and expert contribution requests can then target them.
Representation is evidence about the dataset, not proof that the benchmark is complete. A large or balanced count can still omit a critical boundary. Product teams should use specialist judgment to decide which gaps materially affect trust.
Relationship to correctness and construction
Correctness Elicitation answers what should count as correct in a represented situation. Weave constructs or imports the cases and materials needed to exercise the situation. If the team cannot judge a coverage slice, request an Expert Contribution. If the judgment is clear but no case exists, use Case Foundry or other case-construction work. If cases exist but are not selected, update Benchmark Datasets.
Comparison Directions are not Coverage Facets. They guide comparative response variation and belong to Assets → Comparison Directions. Keep benchmark representation in Dimensions, Topics, Patterns, Stories, and dataset snapshots.
Worked example
Authority-conflict coverage
A project creates source authority and customer impact Dimensions, a Project Topic for eligibility exceptions, and a pattern for pairing current and superseded documents. Coverage Management shows that the high-impact conflict tuple has no selected cases. A Coverage Story justifies the gap, Case Foundry prepares cases, and the accepted cases enter a new dataset snapshot.
Related workflows
Related reference pages
Source confidence
Code-backed: current navigation and benchmark coverage routes establish project Coverage Facets, benchmark Coverage Management, setup, representation, Stories, Case Review, and Case Foundry responsibilities.