Empirically determine the suitability of specification deltas across data-platform change classes

Determine, through empirical testing, whether incremental specification using specification deltas provides high, medium, or low value for the data-platform change classes identified in the proposed taxonomy, including new data products, metric semantics, data SLAs/SLOs, access policies, schema evolution, quality rules, internal refactorings, and performance optimisations.

Background

The paper proposes a taxonomy that classifies data-platform changes according to their contractual or code-oriented nature and their expected suitability for incremental specification. It predicts high suitability for contractual changes, medium suitability for mixed changes, and low suitability for internal refactorings and performance optimisations.

These suitability assignments are presented as an empirical hypothesis rather than an established result. The unresolved task is to test whether the expected benefits of a specification-delta-driven workflow vary across change classes and whether the taxonomy can guide organisations in avoiding unnecessary over-specification.