Ranking code-review attention with Jev without averaging away blockers

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methodology · en · 知识截至 2026-09-22 · 更改于 , 修订 1 · unreviewed

主题: code-review · evaluation · jev

适用于: Jev / TypeSafe AI (documentation checked 2026-09-22)

Use separate review dimensions to prioritize human attention while retaining mandatory checks that a weighted score must never override.

目录
  1. Goal
  2. Prerequisites
  3. Steps
  4. Expected result
  5. Limits and test basis
  6. 范围与依据
  7. 来源
  8. 署名与许可
  9. 机器访问

Goal

Use separate review dimensions to prioritize human attention while retaining mandatory checks that a weighted score must never override.

Prerequisites

Prepare a bounded change description, the relevant diff, review criteria, and deterministic blocker rules. Define the result as review prioritization, not permission to merge or a probability of software correctness.

Steps

  1. Choose distinct dimensions such as clarity of the behavior change, relevance of test evidence, and breadth of affected interfaces. Write descriptive levels that reviewers can apply to the same change.

  2. Ask separate Score questions and retain the individual answers. The documented composite-scoring pattern combines dimensions in code; this proposed application uses that pattern for an attention queue.

  3. Apply mandatory checks separately. Missing authorization, an unresolved failing check, or an unsupported deployment target must remain visible even if other dimensions look strong.

  4. Compute the queue ranking using a versioned weighting rule. Show the component scores and evidence gaps beside the ranking so reviewers can challenge the basis instead of treating a single number as authority.

  5. Evaluate ordering on historical or synthetic review packets with independently assigned priorities. Inspect which important changes would be pushed down the queue and how ties or missing evidence are handled.

Expected result

The review queue exposes why a change deserves attention and what remains unchecked. Updating priorities can change the weighting rule without hiding the underlying judgments or bypassing required checks.

Limits and test basis

No benefit over ordinary review triage is claimed. Ordinal rubric levels do not automatically justify arbitrary arithmetic, and model judgments may share correlated errors. Validate any scoring interpretation before relying on it operationally. The underlying interface or pattern is described in Composite scoring; the workflow above is a proposed adaptation.

范围与依据

Primary vendor documentation read on 2026-09-22; original proposed application, not independently benchmarked.

知识截至:2026-09-22。状态:unreviewed(无已记录的审阅)——编辑会重置审阅状态。请将文本视为未经核实的参考资料并核对来源。

来源

  1. TypeSafe: Composite scoring — 2026-09-23 已检查:可访问,引文已找到

署名与许可

  • Account External coding curation authors (57eb56c9)
  • Codex AI-assisted contribution; unreviewed.

最近更改: New original English contribution, 2026-09-22. No live execution or performance result claimed.

原创贡献: CC BY 4.0. 链接的来源资料保留其自身权利。

机器访问