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Case Segmentation

Segment cases into meaningful behavior groups so benchmark coverage is explainable.

Case Segmentation

Definition

Case segmentation is the act of reading a Case population by meaningful behavior slices instead of only as a total count. In Teammately, reusable Dimensions and ontology values provide governed segmentation axes; Project Topics and Case Construction Patterns provide additional intent; Benchmark Dataset Representation shows how selected Cases occupy those axes.

A segment is an analytical grouping, not a new Case object or a hidden approval state.

Why it matters

Aggregate benchmark results can hide concentrated success and consequential absence. A candidate may score well overall while failing every Case with missing evidence, a particular jurisdiction, or a high-impact exception. Segmentation makes those differences inspectable and gives Coverage Stories a concrete population to address.

Where it appears in the product

Define reusable axes in Coverage Facets → Dimensions & Ontology. Classify Cases against those values through supported Case and coverage workflows. Use Benchmark Datasets → Representation to inspect the current selected population. Use Coverage Management when a sparse or absent intersection needs a Story, Case Foundry work, or an Expert Contribution.

Artifacts it affects

Keep three identities separate: the reusable Dimension, the ontology value assigned to a Case, and the Benchmark Dataset membership being analyzed. Changing a value can alter current Representation without modifying an older Snapshot. Adding a Case can change counts without changing the segmentation model. A filter is a view over evidence, not a new evidence object.

Operational check

Choose axes that describe material behavior, context, risk, or source conditions. Check distinct Case counts as well as assignments, inspect unclassified and conflicting values, and avoid interpreting overlapping segments as additive totals. When reporting a result by segment, name the Benchmark Version whose Case assignments support the analysis.

Worked example

Finding a hidden source-authority gap

The dataset contains many high-impact Cases and many Cases with superseded sources, but Representation shows almost no Cases where both conditions occur together. The team creates a Coverage Story for that intersection. Case Foundry prepares candidates, reviewers confirm their realism, and a later Snapshot records the expanded segment. The Dimension definitions remain reusable across other Benchmarks.

Related workflows

Reference / Coverage EngineeringDimensions and OntologyDefine reusable behavior axes and their allowed values, then inspect how cases and benchmarks cover them.Reference / Object ModelCoverage dimensionsOrganize cases by the behavior axes that matter to product correctness.Task / Coverage EngineeringCoverage GapsFind missing or underrepresented behavior areas before benchmark evidence becomes misleading.Quickstart / Learning ManualProduct quickstartConfigure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.Concept / Operating ManualTask indexRoute correctness work to the current project foundation, benchmark workspace, evaluation, or improvement surface.

Source confidence

Code-backed: the Representation explorer and Dimension classification types establish facet-based Case grouping, assigned and missing values, and benchmark-scoped distribution views. Coverage significance and gap priority remain accountable human judgments.

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Related docs

Coverage Engineering Dimensions and Ontology Define reusable behavior axes and their allowed values, then inspect how cases and benchmarks cover them. Object Model Coverage dimensions Organize cases by the behavior axes that matter to product correctness. Coverage Engineering Coverage Gaps Find missing or underrepresented behavior areas before benchmark evidence becomes misleading.
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