Implement Field Mapping Resolver payload transformation
epic-external-system-integration-configuration-core-services-task-004 — Extend the FieldMappingResolver to apply loaded field mappings during export payload construction. Implements the transformation pipeline that converts internal data model field names to target system field names, handles nested field path resolution, default value injection, and type coercion per system requirements.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 2 - 518 tasks
Can start after Tier 1 completes
Handles integration between different epics or system components. Requires coordination across multiple development streams.
Implementation Notes
Implement transform() as a pure function — it takes a payload map and a FieldMapping and returns a TransformResult. Do not add any state or async logic here. For nested path resolution, split on '.' and recursively build/navigate the output map. Handle the case where an intermediate key already exists and is not a Map (conflict) by producing a TransformError with code PATH_CONFLICT.
For type coercion, implement a private _coerce(dynamic value, TargetType type) function that returns a Result
Testing Requirements
Unit tests using flutter_test. All tests must be synchronous. Required test cases: (1) flat payload with full mapping — all fields renamed correctly, (2) payload with unmapped field in non-strict mode — field passed through, no warning, (3) payload with unmapped field in strict mode — TransformWarning produced, (4) nested dotted path produces correct nested output map, (5) default value injected when field absent, (6) default value NOT injected when field present (even if null), (7) type coercion string→int success, (8) type coercion string→int failure produces TransformError, (9) isoDate coercion from DateTime.toString() format, (10) internal_only field excluded from output. Target 100% branch coverage on the transform() method.
Each of the five external systems (Xledger, Dynamics, Cornerstone, Consio, Bufdir) has a different authentication flow, field schema, and error format. Forcing them into a uniform adapter interface may require compromises that result in leaky abstractions or make the adapter contract too complex to maintain.
Mitigation & Contingency
Mitigation: Design the IntegrationAdapter interface with a loose invoke() payload rather than a typed one, allowing each adapter to declare its own input/output schema. Use integration type metadata in the registry to document per-adapter quirks. Build Xledger first as the most documented API, then adapt the interface based on learnings.
Contingency: If the uniform interface cannot accommodate all five systems, split into two interface tiers: a simple polling/export adapter and a richer bidirectional adapter, with the registry declaring which tier each system implements.
Development and testing of the Cornerstone and Consio adapters depends on NHF providing sandbox API access. If credentials or documentation are delayed, these adapters cannot be validated, blocking the epic's acceptance criteria.
Mitigation & Contingency
Mitigation: Implement Xledger and Dynamics adapters first (better-documented, sandbox available). Create a mock adapter for Cornerstone/Consio using recorded API responses for CI testing. Proactively request sandbox access from NHF at project kickoff.
Contingency: Ship the epic with Cornerstone/Consio adapters in a 'stub' state (connectivity test returns a simulated success, invoke() is not production-wired) and gate the NHF integration behind a feature flag until real API access is obtained.
Real-world field mappings may include nested transformations, conditional logic, and data type coercions (e.g., Norwegian date formats, currency rounding rules) that the Field Mapping Resolver's initial design does not accommodate, requiring scope expansion mid-epic.
Mitigation & Contingency
Mitigation: Gather actual field mapping examples from Blindeforbundet (Xledger) and HLF (Dynamics) before designing the resolver. Identify the most complex transformation required and ensure the resolver design handles it. Limit Phase 1 to direct field renaming and format conversion only.
Contingency: If complex transformations are required, implement a simple expression evaluator (e.g., JSONata or a custom mini-DSL) as an extension point in the resolver, delivering basic mappings first and complex ones in a follow-up task.