Almost every development team claims to do code reviews.
Far fewer can honestly say their reviews consistently improve code quality and developer productivity.
In many teams, code review slowly turns into a source of frustration: pull requests pile up, feedback is inconsistent, and developers either argue over details or stop caring altogether. The problem is rarely the people involved. It is almost always the design of the review process itself.
This article looks at the most common code review problems teams face in real projects and offers practical solutions that work in modern workflows.
Previous article: https://codecraftdiary.com/2025/12/20/development-workflow-failures/
Problem 1: Code Reviews Try to Catch Everything
What Happens
Many teams implicitly expect code review to be the final quality gate. Reviewers are supposed to catch logic bugs, style issues, missing tests, edge cases, performance problems, and architectural flaws - all at once.
The result:
- reviews take too long
- reviewers feel overwhelmed
- authors wait days for feedback
- reviews become superficial under time pressure
Why This Fails
Humans are bad at repetitive, mechanical checks. When reviewers are overloaded, they either miss important issues or focus on trivial ones.
Expecting reviewers to act as both automation and architects guarantees burnout.
Example
A typical pull request in such teams looks like this:
The CI pipeline is minimal or missing, so reviewers leave comments about formatting, unused imports, naming conventions, and missing null checks. By the time anyone looks at the actual behavior of the code, the discussion thread already has 20 comments.
The same change with proper automation looks very different. Formatting, linting, and tests are enforced automatically. Review comments focus on edge cases, error handling, and whether the change actually solves the problem it claims to solve.
Nothing about the developers changed. Only the responsibility of the review did.
The Fix: Narrow the Responsibility of Code Review
A healthy process clearly defines what code review is not responsible for.
Anything that can be checked automatically should never be reviewed manually:
- formatting and style
- linting and static analysis
- basic type or syntax errors
- test execution
Once automation enforces the baseline, reviewers can focus on:
- correctness of behavior
- clarity of intent
- risk and edge cases
Code review should reduce risk - not eliminate it.
Problem 2: Reviews Focus on Opinions Instead of Outcomes
What Happens
Comments like:
- "I would write this differently"
- "This doesn't feel clean"
- "I don't like this abstraction"
Example
Opinion-based feedback often sounds like this:
"I would structure this differently."
Outcome-based feedback reframes the same concern:
"This structure makes it hard to see how errors propagate. How would we diagnose a failure here in production?"
Reviews turn into subjective debates. Decisions depend on who reviews the PR rather than on shared standards.
Why This Fails
Opinion-driven reviews scale poorly. As teams grow, inconsistency increases and trust erodes. Authors stop learning because feedback feels arbitrary.
The Fix: Anchor Reviews to Explicit Criteria
Effective reviews are guided by shared questions, not personal taste.
Examples of outcome-based review questions:
- Is the intent of this change clear from the code and description?
- Does this implementation handle failure cases explicitly?
- Does this introduce hidden coupling or long-term maintenance risk?
- Is the complexity justified by the problem being solved?
When feedback is tied to outcomes, disagreements become productive discussions instead of personal conflicts.
Problem 3: Pull Requests Are Too Large
What Happens
Large pull requests accumulate:
- multiple features
- refactors mixed with behavior changes
- unrelated fixes "while I was there"
Reviewers skim. Important issues get missed. Feedback arrives late or not at all.
Why This Fails
Large PRs increase cognitive load. Reviewers cannot hold the full context in their heads, especially under time pressure.
Example
A single pull request includes:
- a new feature
- a refactor of existing logic
- renamed variables across multiple files
- a few unrelated fixes "while touching the code"
Reviewers now have to understand what changed and why, across multiple concerns. Important issues are missed simply because the mental load is too high.
When the same work is split into three focused pull requests, reviews become faster, feedback is clearer, and risk is reduced - even though the total amount of code is the same.
The Fix: Enforce Small, Focused Changes
A good rule of thumb:
If a reviewer cannot understand the purpose of a PR in five minutes, it is too large.
Practical improvements:
- separate refactors from functional changes
- ship incremental improvements behind feature toggles if needed
- encourage early, partial PRs for feedback
Small PRs are not about discipline. They are about enabling fast feedback loops.
Problem 4: Reviews Ignore Failure Scenarios
What Happens
Reviews focus on the happy path:
- valid input
- successful API calls
- ideal system state
Edge cases and failure modes are rarely discussed.
Why This Fails
Most production incidents are caused by:
- unexpected input
- partial failures
- timeouts and retries
- concurrency issues If these are not considered during review, they will surface later - in production.
Example
A change integrates with an external API. The review verifies that the happy path works and approves the pull request.
In production, the API occasionally times out. The retry logic triggers twice, causing the same operation to run multiple times. Duplicate records appear. The issue is not a bug in the implementation - it is a missing discussion about failure modes during review.
Asking "What happens if this runs twice?" during review would have surfaced the problem early.
The Fix: Make Failure a First-Class Review Topic
Reviewers should explicitly ask:
- What happens if this dependency fails?
- What happens if this runs twice?
- What happens under load?
- What assumptions does this code rely on?
Even if not all cases are handled immediately, acknowledging them dramatically reduces future surprises.
Problem 5: Tests Exist but Don't Prove Anything Important
What Happens
Tests are present, but they:
- assert implementation details
- mirror the code instead of validating behavior
- break frequently during refactors
Reviews approve tests because "there are tests", not because they add confidence.
Why This Fails
Decorative tests create a false sense of security. They increase maintenance cost without reducing risk.
The Fix: Review Tests as Risk Protection
Example
A test asserts that a private method is called with specific parameters. The test passes, but it does not verify any observable behavior.
After a refactor, the test breaks even though the system still behaves correctly. Developers update the test without gaining any new confidence.
A single test that verifies a business rule - for example, "an order is rejected when inventory is insufficient" - would provide far more value than multiple tests that mirror the internal structure of the code.
Instead of counting tests, reviewers should ask:
- What behavior would break if this test failed?
- Does this test protect a business rule or just code structure?
- Would this test catch a real regression?
A small number of meaningful tests is more valuable than broad but shallow coverage.
Problem 6: Code Review Culture Becomes Toxic or Avoidant
What Happens
Two common extremes:
- overly aggressive reviews that discourage contributions
- overly polite reviews that avoid real feedback
Both lead to stagnation.
Why This Fails
Code review is a social process. If feedback feels unsafe or pointless, developers disengage.
The Fix: Separate People from Code - Explicitly
Healthy teams adopt clear norms:
- critique decisions, not individuals
- explain why something matters
- acknowledge good solutions
- use questions to invite discussion
Code review should feel like collaboration, not judgment.
Problem 7: Approval Means "Perfect"
What Happens
Reviewers hesitate to approve because they see possible improvements. PRs stall waiting for "one more fix".
Why This Fails
Perfection is undefined and unattainable. Delayed merging increases risk more than minor imperfections.
The Fix: Redefine Approval
Approval means:
"The risk of merging this change is acceptable given our context."
It does not mean:
- the solution is ideal
- no improvements remain
- no future refactoring will be needed
This shift alone can dramatically speed up delivery without sacrificing quality.
How This Fits Modern Development Workflows
In modern teams:
- automation enforces consistency
- deployments are frequent
- rollbacks are possible
- feedback cycles are short
Code review should complement these realities - not fight them.
When designed correctly, code review:
- spreads knowledge
- reduces hidden risk
- improves decision-making
- strengthens team trust
When designed poorly, it becomes friction disguised as quality control.
Final Thought
Most code review problems are not technical.
They are process and expectation problems.
Fixing them does not require smarter developers or stricter rules - only clearer boundaries, better questions, and respect for how people actually work.
When that happens, code review stops being a bottleneck and becomes what it was always meant to be: a shared investment in better software.
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