Eval datasets for coding agents and repository tasks

What matters first

Coding-agent eval datasets should look like repository work, not like generic coding questions.

That means examples should encode:

realistic file scope,
repository constraints,
approval boundaries,
tests or checks that matter,
and the kinds of ambiguity engineers actually face in real change requests.

Why benchmark-style prompts are not enough

A toy coding prompt may show whether a model can generate code. It says much less about whether an agent can:

navigate a real repository,
stay inside the allowed scope,
choose the right files,
avoid touching risky paths,
and stop when the task needs human judgment.

That is why repository-aware eval data matters.

What a healthy coding-agent eval example includes

Each example should usually define:

the task request,
the allowed write scope,
the forbidden paths,
the expected verification or checks,
and the acceptable end state.

Without those, the eval mostly measures coding fluency rather than operational discipline.

Dataset example schema

Field	What to capture	Why it matters
Task request	The real user or ticket-style instruction	Preserves ambiguity and intent shape
Repository context	Relevant files, entry points, tests, and ownership	Tests navigation, not just code writing
Allowed scope	Paths, commands, and operations the agent may use	Measures scope discipline
Forbidden scope	Sensitive paths, tools, secrets, infra, or merge actions	Tests approval and refusal behavior
Expected checks	Unit tests, lint, type check, screenshot, build, or domain validation	Measures verification behavior
Expected outcome	Patch, explanation, PR, refusal, escalation, or clarification	Prevents every task from being judged as “produce code”
Failure labels	Wrong file, wrong tool, missing test, scope expansion, approval miss, bad patch	Routes failures to the right owner

This schema is the practical value of the page: it gives eval owners a repeatable format for turning repository work into measurement data.

The most valuable example classes

High-yield dataset classes usually include:

Local bounded change

A small feature or fix in one narrow scope.

The agent must find the right place to work without expanding scope unnecessarily.

Approval-sensitive change

The task appears small but touches CI, dependencies, or another sensitive file class that should trigger stronger approval.

Test-alignment task

The task requires updating or adding tests in a way that reflects the change instead of editing snapshots blindly.

Refusal or escalation case

The correct behavior is not to proceed automatically.

These classes reveal whether the agent behaves like a safe repository operator, not just a code generator.

The dataset design rule

Use examples pulled from real engineering work whenever possible:

anonymized past tasks,
representative bug classes,
common refactor requests,
and real review comments that caused production confusion.

That produces a dataset with much higher operational value than synthetic prompt-only tasks.

What to score

For coding-agent eval datasets, score at least:

file/path selection,
scope discipline,
policy compliance,
verification behavior,
and final correctness.

The change itself matters, but so does the path the agent used to get there.

What teams usually miss

Teams often omit negative examples:

tasks that should be refused,
tasks that should be escalated,
or tasks where the request is underspecified.

Those are crucial because repository safety depends on restraint as much as capability.

Implementation checklist

Your coding-agent eval dataset is probably healthy when:

examples look like real repository work;
path and policy constraints are explicit;
escalation cases are included;
and success requires more than producing plausible code.

Compare next

Reader value check

This page should help a reader decide which repository actions a coding agent should be allowed to take and which gates must protect shared code. For Eval datasets for coding agents and repository tasks, the page is not finished if it only explains vocabulary. It should change what the team approves, measures, routes, buys, logs, or refuses to automate.

Before applying the guidance, bring changed files, test results, reviewer queue data, PR outcomes, and examples of bad or reverted agent changes. Those inputs keep the decision anchored in real operating conditions instead of a generic best-practice list.

Check	What the reader should be able to answer
Repository boundary	Does the page separate read, write, review, merge, and deploy risk?
Reviewer load	Does it account for the human time needed to inspect generated work?
Verification	Are tests, static checks, and PR gates tied to the action being approved?
Rollback	Can the team undo or contain the change if the agent is wrong?

Use the page as a working review artifact: compare the current workflow against the table, mark the missing evidence, and assign an owner for the next change. If the page exposes a gap but no one owns that gap, the correct next step is not broader rollout; it is a smaller pilot, a clearer gate, or a better measurement loop.

For coding-agent pages, the reader should be able to turn the guidance into a repo policy, PR checklist, or reviewer queue rule. Broad enthusiasm is not enough when the output enters shared code.