v1.0 stability and defect burn-down

Problem

After public launch, the profiling pipeline will accumulate defects from diverse warehouse environments, schema shapes, and usage patterns that internal testing did not cover. Without a structured stability program — recurring defect triage, burn-down tracking, and reliability trend monitoring — regressions and edge-case failures will pile up silently. Users will encounter intermittent profiling errors, incorrect semantic type assignments on uncommon data patterns, or rendering glitches in the inspector report, and the team will have no visibility into whether reliability is improving or degrading over time. A formal stability cadence is needed to sustain user confidence in the v1.0 release.

Context

• After launch, recurring defects in warehouse profiling, semantic inference, and analyst-facing inspect/context artifacts will damage trust faster than new features can restore it, so this phase should prioritize stability over new scope.
• The goal is to identify the repeat offenders, remove the highest support burden, and make failure patterns measurable enough that the team knows whether quality is improving.
• Expected touchpoints include dataface/core/inspect/, schema-context consumers, inspect docs, and core tests, bug history, support or incident notes, and any tests or QA gaps that let defects recur.

Possible Solutions

• A - Keep mixing bug fixes with feature work opportunistically: preserves flexibility, but lets long-tail reliability work stay perpetually unfinished.
• B - Recommended: run an explicit stability program: rank defect classes, burn down the highest-frequency issues, and pair fixes with validation so regressions stop recurring.
• C - Freeze all new work until zero known defects remain: simple in principle, but unrealistic and usually counterproductive.

Plan

1. Aggregate the recurring failures in warehouse profiling, semantic inference, and analyst-facing inspect/context artifacts from bugs, support notes, and recent releases, then rank them by user impact and repeat rate.
2. Turn the top defect classes into a concrete burn-down list with owners, acceptance criteria, and the validation needed to keep each fix from regressing.
3. Land or schedule the highest-leverage fixes first, including any docs or operator changes that reduce repeat incidents.
4. Review the remaining defect mix after the first burn-down pass and update the next tranche of work based on actual stability improvements.

Implementation Progress

Review Feedback

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