How do YOU decide a PR actually needs deeper review?

[hensed]

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I’m curious how experienced reviewers make this call in practice.

As repos scale, I’ve noticed PR reviews often drift toward:
• surface-level comments
• checklist validation
• fast “LGTM” approvals

But the bugs that hurt us most usually weren’t from untested code, they came from PRs where:
• a “reasonable” change affected behavior downstream
• business logic moved in subtle ways
• tests existed, but didn’t protect what mattered

When you’re reviewing a PR, what makes you stop and think
“this one deserves extra scrutiny”?

Is it:
• specific paths or folders?
• core business logic vs plumbing?
• change frequency or churn?
• missing tests on certain surfaces?

I’m less interested in tools and more interested in how humans actually make this judgment today.

Would love to hear how others approach this.

[Ayla-hmadi]

Red Flags That Warrant Deep Scrutiny

1. Behavioral Changes Disguised as Refactors

• PR title says "refactor" but logic is subtly different
• "Just moving this code" but the execution order changed
• Extract-method that changes variable scope or timing
• Why it matters: These are where the sneakiest bugs hide

2. Changes to Code with High Fan-Out

• Utilities used everywhere
• Shared validators or transformers
• Base classes or interfaces
• Middleware or interceptors
• The heuristic: "How many callsites could break if this assumption changes?"

3. Touching Money, Auth, or Data Integrity

Automatic deep review for anything involving:

• Payment flows
• Permission checks
• Data deletion/modification
• Rate limiting
• Data migration scripts

Even if tests pass, think: "What happens if this runs twice?" "What if the input is null?" "What permissions does this bypass?"

4. The "Small" Change to a Critical Path

• One-line change in checkout flow
• "Quick fix" in authentication
• Pattern: Small diff, huge blast radius
• These deserve 10x more scrutiny than their line count suggests

5. Changes Without Corresponding Test Changes

Not just "are there tests?" but:

• Business logic changed but tests didn't?
• New edge case but no new test case?
• Error handling added but not tested?

The question: "If I introduced the bug this PR claims to fix, would the new tests catch it?"

Mental Models Experienced Reviewers Use

"What Could This Break?"

• Downstream callers: Who depends on this behavior?
• Implicit contracts: What assumptions do other parts of the system make?
• Edge cases: What inputs would expose this?

"What's NOT in the PR?"

• Missing database migrations for schema changes
• Missing rollback plan for risky changes
• Missing feature flags for gradual rollout
• Documentation not updated

"The Story Doesn't Add Up"

• PR description says "add logging" but also touches business logic
• "Fix typo" but changes method signatures
• Lots of unrelated files changed
• Action: Ask "Why did you also change X?"

Specific Patterns That Demand Attention

High-Risk Code Patterns

// Alarm bells:
- Changing SQL WHERE clauses
- Modifying regex patterns (especially validation)
- Touching date/time arithmetic
- Changes to retry/timeout logic
- Async/await added or removed
- Changing loop conditions or iteration order
- Modifications to caching logic

Structural Red Flags

• Many files changed, small diff per file: Logic is being threaded through the system
• New dependencies: Supply chain risk, bundle size, maintenance burden
• Changes to CI/CD or deployment scripts: Can break everyone's workflow
• Database schema changes: Need migration strategy and rollback plan

Practical Heuristics

The "3 AM Production Fire" Test

Ask yourself: "If this breaks in production at 3 AM, how hard is it to:

• Understand what broke?
• Identify the root cause?
• Roll back safely?
• Debug with available logs?"

If any answer is "very hard," that's a red flag.

The "6 Months From Now" Test

"Will I understand this code in 6 months when:

• The author is on vacation?
• I've forgotten the context?
• Requirements have changed?"

The Chesterton's Fence Principle

When seeing deleted code or "cleanup": Stop and ask why it was there.

• Was it handling an edge case?
• Working around a platform bug?
• Required for a specific client?

What Makes You Pause?

In practice, I stop for deeper review when:

1. The author is junior (not their fault - they don't know what they don't know)
2. The author is very senior (they might be moving fast and assuming context)
3. PR created Friday afternoon (cognitive bias toward "ship it")
4. Long-lived branch merged (likely merge conflicts hiding behavioral changes)
5. "Hot fix" label (pressure to skip scrutiny)
6. Large PR split into smaller ones (hard to see full picture)

Questions to Ask in Review

Instead of just commenting, ask:

• "What happens if this service is down when this code runs?"
• "How does this behave with 10,000 items instead of 10?"
• "What if two requests call this simultaneously?"
• "Does this handle the case where [assumption] is false?"
• "What's the rollback plan if this causes issues?"

The Meta-Pattern

The PRs that hurt most are the ones that look safe.

The 5-line "obvious" fix to a core utility. The "minor refactor" that changes execution timing. The "cleanup" that removes defensive code someone added for a reason.

Building Intuition

This judgment comes from:

• Scars: You've been burned by "safe" changes before
• System knowledge: You know where the landmines are
• Incident retrospectives: Patterns emerge from post-mortems
• Asking "why?": Don't accept "it works" - understand why it works

---

Bottom line: Deep review when the risk is non-obvious or the blast radius is large. Surface-level review is fine for isolated changes with clear scope. The hard part is distinguishing between them.

What's your team's scariest "looked safe in review" story?

[QE-Sentinel]

This is one of the clearest articulations I’ve seen of how experienced reviewers actually think. Especially the line: “The PRs that hurt most are the ones that look safe.”

What strikes me is how much of this is about non-obvious risk — blast radius, behavioral drift, missing context — not code quality in the narrow sense. Curious: how do you scale this kind of judgment across a team without burning out your most senior reviewers?

[gantoine]

If I wanted a useless AI summary I'd ask ChatGPT myself...

[qe-tester]

@gantoine Sometimes folks have trouble articulating. ;)

[santiagomorenoe]

how do you determine the 'stopping point' for a deep review? I’m interested in how teams balance the need for deep architectural checks (avoiding that hidden risk mentioned) without creating a bottleneck in the PR pipeline. Is there a specific signal you use to decide if a PR needs a 'deep dive' vs a standard check

[riteshroshann]

What makes experienced reviewers pause isn’t code complexity, it’s systemic risk. The best signal for “deeper review required” is not the diff size, and not the PR title, but whether the change alters an implicit contract somewhere in the system. Those contracts tend to sit in core utilities, business invariants, data flows, permission boundaries, and components with high fan-out. Tests rarely encode those assumptions, which is why a PR can be fully covered and still ship a production incident.

The meta-heuristic is: “If this behavior changed silently, how wide would the blast radius be, and how long until someone noticed?” When the answer is “large and delayed,” you slow down.

Scaling this beyond a few senior reviewers requires externalizing that tacit knowledge: incident postmortems, architectural decision records, code ownership maps, and risk catalogs give juniors a scaffold to develop the same intuitions. The bottleneck isn’t review bandwidth, it’s institutional memory. Out of curiosity—does your team currently document implicit contracts explicitly, or are they still tribal knowledge encoded in the heads of the few who’ve been burned before?

[QE-Sentinel]

yup! this hits... The “implicit contract” framing is exactly where most reviews fall down.

I like the idea that tests rarely encode those assumptions, which is why coverage can be green while risk is still very real. I’ve seen incidents where everything that was tested passed but what broke was an expectation no test ever described.

The point about institutional memory is huge too. In practice, the reviewers with the best intuition are the ones who’ve lived through outages, rollbacks, and awkward postmortems and that knowledge rarely gets externalized in a way the rest of the team can actually use during review.

Do you seen teams make that knowledge actionable at review time (not just documented somewhere). Do you rely more on ownership boundaries, review checklists, or “stop-and-think” prompts when certain surfaces are touched?

[XusanDev07]

Pause for a deep PR review when there’s a large/complex diff, API/DB or core business logic changes, cross-cutting concerns (auth/caching/migrations), insufficient or unclear tests, high churn, or new dependencies

[QE-Sentinel]

This is a solid checklist. what jumps out to me is that almost all of these signals are really proxies for the same underlying thing: blast radius + uncertainty. Large diffs, core business logic, auth/DB changes, high churn, new dependencies they all increase either how much could break, or how hard it would be to reason about what breaks. What I’ve found helpful in practice is asking one question that collapses the list:

“If this change behaves differently than expected, how wide is the impact — and how quickly would we notice?”

When the answer is “wide and delayed,” that’s the moment to slow down, regardless of whether the diff is big or small. Some of the scariest PRs I’ve seen were tiny changes in high-fan-out code that looked totally reasonable on the surface.

Does YOUR team have a shared way to flag those cases early, or if it still depends on someone recognizing the pattern by experience.

[XusanDev07]

I wouldn’t say that we have a strict standard for this yet, but for now that’s the situation. We’ll start paying more focused attention to it in the near future.

As you mentioned, at the moment this still relies on someone’s experience. However, on the backend side, reviews are done more thoroughly compared to the frontend. In particular, a merge only happens after several people have reviewed the change, and it goes through various types of tests before that.

[QE-Sentinel]

...yea That totally tracks, and it mirrors what I’ve seen across a lot of teams.

What’s interesting is that right now the rigor comes from people and process more reviewers, more gates, more tests especially on the backend where blast radius is obvious. That works, but it still depends heavily on who’s reviewing and how much context they carry.

The hard problem isn’t review effort, it’s knowing where to slow down. Some PRs deserve deep scrutiny even if they’re small; others don’t, even if they touch many files. Without an explicit risk model, that judgment stays tribal.

This is actually the gap I started building QE Sentinel around. externalizing senior QE intuition into explainable signals so teams can decide when to apply that extra rigor, not just apply it everywhere.

Curious whether you expect frontend to converge toward backend-style review rigor over time, or whether you see them needing fundamentally different risk models.

[XusanDev07]

QE Sentinel sounds like an interesting project.

To answer your question, I expect frontend to gradually adopt stricter reviews over time, because right now we don’t have a dedicated person checking frontend. If QE Sentinel turns out to be useful for frontend, then we’ll give it a try ;)

[QE-Sentinel]

frontend risk is often harder to reason about without a dedicated reviewer. QE Sentinel is aimed at surfacing “this deserves a deeper look” signals in those PRs.

Appreciate you taking a look. If you’re open to it, I’m happy to get you and/or your team free usage on your account/repo in exchange for a paragraph of honest feedback on whether it’s useful for your team.

[QE-Sentinel]

Quick update — we developed a tool around a lot of the PR-risk patterns discussed here.
It’s now live on the GitHub Marketplace. If anyone’s curious to try it on a repo, feedback would be of great value!

https://github.com/marketplace/qe-sentinel

[QE-Sentinel]

how do you determine the 'stopping point' for a deep review? I’m interested in how teams balance the need for deep architectural checks (avoiding that hidden risk mentioned) without creating a bottleneck in the PR pipeline. Is there a specific signal you use to decide if a PR needs a 'deep dive' vs a standard check

@santiagomorenoe This is the hardest part of review for real...

For me, the stop point isn’t about time or PR size, it’s about whether I feel I’ve reconstructed the risk in my head. our PRs usually escalate to a deep dive when one or more of these show up:
• behavior changes without an obvious test that would fail if it regressed
• logic moves across boundaries (modules, layers, ownership lines)
• simple conditionals on paths that run frequently or affect money / auth / data
• changes that are hard to reason about locally (order, state, config, async)

If I can’t explain what would break and how we’d notice after reading the PR, I’m not done reviewing, even if all the checks are green. Teams that avoid bottlenecks tend to agree upfront on which changes automatically trigger this level of scrutiny, so it’s expected not personal.

[Sabari-Vasan-SM]

For me, a PR needs deeper review when it changes behavior, not just structure. Renames, refactors, or config tweaks are usually quick, but anything that touches core business logic, decision-making paths, or data flow makes me slow down.

A few things that trigger extra scrutiny:

Logic moving across layers (service ↔ domain ↔ API)

Changes that affect defaults, edge cases, or “what happens when X is missing”

Tests that exist but only cover the happy path

Code that looks small but sits in a high-impact area

Basically, if a change could surprise another part of the system later, that’s when I stop treating it as an LGTM and start reasoning through scenarios