AI coding assistants can generate code, write tests, fix bugs, and prepare pull requests in a fraction of the time required for many manual tasks. However, faster code generation does not automatically mean better software.
An AI-generated pull request can compile successfully and pass existing tests while still introducing security vulnerabilities, incorrect business logic, performance problems, or changes that break existing functionality. These problems can be difficult to detect when reviewers focus only on whether the code looks correct.
The solution is not to reject AI-generated code. Instead, development teams need a review process that evaluates AI-generated changes with the same engineering standards applied to manually written code.
In this article, we will explore how to review AI-generated pull requests, identify common risks, automate important checks, and establish a practical review process before merging changes into a production branch.
Why AI-Generated Pull Requests Need Careful Review
AI coding tools generate code based on the context, instructions, and information available to them. They can produce useful implementations, but they do not automatically understand every business requirement, production constraint, or architectural decision in a repository.
For example, an AI assistant might generate an API endpoint that returns customer information. The endpoint could compile and pass basic tests while failing to verify whether the requesting user is authorized to access the requested customer record.
Similarly, an AI-generated database query might work correctly with a small development dataset but perform poorly when production data grows.
Common risks include:
- Incorrect business logic that passes superficial testing.
- Missing authorization checks or inadequate input validation.
- Insecure handling of credentials and sensitive information.
- Database queries that become expensive at scale.
- Incomplete exception handling and resource cleanup.
- Tests that verify implementation details but miss important requirements.
- Unnecessary dependencies or architectural changes.
- Incorrect assumptions about existing APIs or library behavior.
These risks also exist in manually written code. The difference is that AI tools can generate large changes quickly, making it easier for reviewers to overlook defects when the pull request contains many unrelated modifications.
The objective of the review process is to verify correctness, security, maintainability, and compatibility rather than simply checking whether the generated code appears reasonable.
Start by Understanding the Purpose of the Pull Request
Before examining individual lines, understand what the pull request is supposed to accomplish.
Read the pull request description, linked issue, acceptance criteria, and relevant design decisions. Identify the expected behavior and the files that should reasonably change.
Ask the following questions:
- What problem is the change intended to solve?
- Which application components are affected?
- What behavior should remain unchanged?
- Does the implementation modify public APIs or database schemas?
- Are authentication, authorization, or sensitive data involved?
- What tests demonstrate that the requirements are satisfied?
This initial review helps identify changes that do not belong in the pull request.
For example, suppose a pull request is intended to add pagination to a product API. The AI assistant also changes authentication middleware, upgrades several unrelated packages, and modifies database initialization.
Those additional changes increase the review scope and may introduce unnecessary risk.
Request that unrelated changes be separated unless they are required for the feature. Smaller pull requests are easier to understand, test, review, and roll back.
Review the Diff Before Reviewing Individual Methods
The pull request diff shows exactly what changed compared with the target branch.
Start by reviewing the overall change summary, then inspect the modified files.
For a Git-based repository, the following command compares the current branch with the target branch:
git diff --stat origin/main...HEAD
To inspect the complete patch, run:
git diff origin/main...HEAD
These commands assume that the target branch is named main and the latest remote references have been fetched.
Look for unexpected changes to configuration files, dependency manifests, authentication logic, database migrations, infrastructure definitions, and CI/CD workflows.
Review deleted code as carefully as added code. AI-generated changes may remove validation, error handling, logging, or compatibility logic that appears unnecessary in the immediate context but is important elsewhere.
A useful rule is to understand why each meaningful change exists before approving the pull request.
Check Business Logic and Edge Cases
Code that looks clean can still implement the wrong behavior.
Review the implementation against the actual requirements rather than judging it only by style or structure.
Consider a simple ASP.NET Core endpoint that retrieves an order:
app.MapGet(
"/orders/{id:int}",
async (
int id,
IOrderService orderService,
CancellationToken cancellationToken) =>
{
var order = await orderService.GetByIdAsync(
id,
cancellationToken);
return order is null
? Results.NotFound()
: Results.Ok(order);
});
This code illustrates a basic retrieval endpoint. Whether it is correct depends on the surrounding application.
A reviewer should investigate several questions:
- Does the service verify that the current user can access this order?
- Is the order ID validated according to the application's requirements?
- Does the service handle database failures appropriately?
- Does the endpoint expose sensitive information?
- Are cancellation tokens propagated to database operations?
- Are not-found and unauthorized cases handled consistently?
The endpoint may be perfectly valid for an internal administrative service with authorization enforced elsewhere. It may be unsafe if no authorization check exists anywhere in the request pipeline.
The important task is to verify the complete execution path, not assume that every method must contain all security checks itself.
Examine Boundary Conditions
Review how the code behaves when inputs are empty, missing, malformed, unexpectedly large, or outside the expected range.
For data-processing code, consider duplicate records, missing fields, invalid states, and partial failures.
For asynchronous operations, inspect cancellation, timeouts, concurrency, and resource cleanup.
For financial or business-critical calculations, independently verify the formulas and rounding rules instead of trusting generated explanations.
Review Security-Sensitive Changes
Security review deserves particular attention when AI-generated code handles user input, credentials, identity, database access, file operations, or external requests.
Verify Authentication and Authorization
Authentication determines who is making a request. Authorization determines whether that identity is permitted to perform the requested operation.
Check that the application applies the correct authorization policy to new endpoints and sensitive operations.
For example, an endpoint that retrieves an invoice should not rely solely on a valid user session. It may also need to verify that the invoice belongs to the user's organization or account.
Look for object-level authorization problems, missing permission checks, and accidental exposure of administrative functionality.
Check Input Validation and Injection Risks
Inspect database queries, shell commands, file paths, and dynamically constructed URLs.
Parameterized database queries should be preferred over concatenating untrusted input into SQL statements.
For example, a parameterized query using Entity Framework Core might look like this:
var customer = await dbContext.Customers
.Where(customer => customer.Id == customerId)
.FirstOrDefaultAsync(cancellationToken);
The query uses a LINQ expression that Entity Framework Core translates into a database query with appropriate parameter handling.
However, safe query construction does not replace authorization checks or business validation. A syntactically safe query can still return data that the caller should not be allowed to access.
Inspect Secrets and Sensitive Data
Search the changes for API keys, passwords, connection strings, tokens, and private certificates.
AI-generated code should retrieve credentials through the application's approved secret-management mechanism rather than embedding them in source code.
Also inspect logs and exception responses. A change that logs complete authentication headers or returns internal exception details can expose sensitive information.
Verify Dependencies and Configuration Changes
AI coding assistants may introduce new packages when an existing library already provides the required functionality.
Review every dependency addition or version change.
Check whether the package is maintained, compatible with the project's target framework, appropriate for the application's license requirements, and necessary for the feature.
For .NET projects, inspect the project file and run dependency checks:
dotnet list package --vulnerable --include-transitive
This command helps identify known vulnerabilities in resolved NuGet dependencies, subject to the installed SDK's supported command syntax and available vulnerability information.
Also inspect configuration changes involving CORS, authentication schemes, TLS settings, environment variables, logging levels, and database connections.
A change that works on a developer's machine may fail in production because it relies on local configuration or assumes that a service is always available.
Evaluate the Tests Instead of Counting Them
A pull request may add several tests without adequately verifying the feature.
Review what the tests actually prove.
Good tests should cover expected behavior, invalid inputs, failure conditions, authorization boundaries, and important regression scenarios.
For example, a test for an order endpoint should not only verify that an existing order returns a successful response. It should also verify how the application behaves when the order does not exist and when the current user is not authorized to access it.
Look for tests that simply reproduce the implementation's assumptions.
If an AI assistant generates both the implementation and the tests, the tests may repeat the same incorrect assumption. Reviewers should derive important test cases from the requirements independently.
Run the relevant tests locally or in CI:
dotnet restore
dotnet build --no-restore
dotnet test --no-build
These commands restore dependencies, compile the solution, and execute tests. If the repository contains multiple solutions or requires integration-test services, adjust the commands to match the project.
A successful test run is evidence of correctness, not proof that every possible defect has been eliminated.
Automate Checks in the Pull Request Pipeline
Manual review is essential, but automated checks can identify many problems before a reviewer examines the code.
A GitHub Actions workflow can run the build, tests, and dependency checks whenever a pull request is opened or updated.
name: Pull Request Validation
on:
pull_request:
branches:
- main
permissions:
contents: read
jobs:
validate:
runs-on: ubuntu-latest
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Set up .NET
uses: actions/setup-dotnet@v4
with:
dotnet-version: '10.0.x'
- name: Restore dependencies
run: dotnet restore MyApplication.sln
- name: Check vulnerable packages
run: dotnet list MyApplication.sln package --vulnerable --include-transitive
- name: Build solution
run: dotnet build MyApplication.sln --no-restore
- name: Run tests
run: dotnet test MyApplication.sln --no-build
This is a starting point, not a complete security gate. The SDK version must match the project, and the workflow should use the repository's actual solution name.
The vulnerability-reporting command may report findings without automatically failing the workflow. If vulnerable dependencies must block a merge, configure an explicit audit policy or a dedicated dependency scanning tool that enforces the intended threshold.
For production workflows, also pin third-party actions to reviewed full commit SHAs and configure required status checks through repository rules.
Additional checks can include static analysis, secret scanning, license compliance, container scanning, and infrastructure configuration validation.
Automation should support human review rather than replace it.
Use a Structured Review Checklist
A consistent checklist makes it easier to review AI-generated pull requests without focusing exclusively on code style.
Review area | Questions to answer |
|---|
Requirements | Does the implementation satisfy the acceptance criteria? |
Scope | Are the changes necessary and limited to the intended feature? |
Correctness | Have edge cases and failure conditions been considered? |
Security | Are authentication, authorization, validation, and secret handling correct? |
Performance | Could the change introduce expensive queries or unnecessary processing? |
Testing | Do tests cover expected behavior and important failure scenarios? |
Dependencies | Are new packages necessary and appropriately maintained? |
Compatibility | Could existing clients, APIs, or database consumers break? |
Observability | Are failures diagnosable without exposing sensitive information? |
Deployment | Are configuration changes, migrations, and rollback requirements understood? |
Not every item requires the same level of investigation for every pull request. A documentation change has different risks from a change to payment processing or authentication.
Scale the depth of review according to the impact of the change.
Common Mistakes to Avoid
One common mistake is approving a pull request because the code compiles. Compilation does not validate business rules, authorization, or production behavior.
Another is trusting generated tests without examining their assumptions. Tests written from the same incorrect interpretation as the implementation may pass while the feature remains defective.
Reviewers also sometimes focus on formatting and naming while overlooking important changes to security configuration or database behavior.
Finally, avoid assuming that an AI-generated explanation proves the implementation is correct. Treat explanations as review aids and verify the actual code, documentation, and behavior.
Advantages and Limitations of AI-Assisted Review
AI-assisted review can help identify suspicious patterns, suggest missing test cases, explain unfamiliar code, and summarize large diffs. It can be particularly useful when a change affects multiple files or unfamiliar parts of a repository.
However, AI review tools can miss defects, produce false positives, or recommend changes that do not fit the application's architecture. Their conclusions depend on the context they receive and the capabilities of the tools being used.
Use AI as an additional reviewer, not as the final authority. Human reviewers remain responsible for evaluating business requirements, security implications, architectural decisions, and release risk.
Summary
Reviewing AI-generated pull requests requires the same engineering discipline as reviewing any other code, with particular attention to large diffs, hidden assumptions, and incomplete tests.
Start by understanding the intended change and inspecting the full diff. Verify business logic, authorization, input validation, dependency changes, and failure handling. Run automated checks and confirm that tests cover important edge cases rather than simply reproducing the implementation.
Use a structured checklist and apply stricter review requirements to security-sensitive or business-critical changes.
AI coding tools can accelerate development, but production quality still depends on careful verification, reliable testing, and accountable engineering decisions.
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