AI Code Review: 10 Practical Ways AI Can Help Developers Find Bugs and Improve Code

Code review is an important part of software development.

A developer may write code that compiles successfully and passes basic tests but still contains problems such as unclear logic, duplicated code, missing edge cases, security weaknesses, or difficult-to-maintain design.

Traditional code reviews rely heavily on another developer manually examining the code.

AI can provide an additional review layer.

An AI coding assistant can examine code, explain potential problems, suggest improvements, identify possible edge cases, and help developers think about questions they may have overlooked.

But AI code review should not replace experienced developers or automated testing.

Instead, think of it as another pair of eyes.

In this guide, we will explore 10 practical ways developers can use AI for code review, with reusable prompts, examples, a practical review workflow, common mistakes, and guidelines for using AI responsibly.

What Is AI-Assisted Code Review?

AI-assisted code review is the use of artificial intelligence to analyze source code and provide suggestions or identify potential issues.

Depending on the tool and context, AI may help identify:

• Potential bugs
• Readability problems
• Duplicated logic
• Missing error handling
• Security concerns
• Performance issues
• Edge cases
• Maintainability problems
• Testing gaps
• Documentation opportunities

AI can review a small method or a much larger change, depending on the capabilities of the tool.

The quality of the review depends heavily on the context provided to the AI.

A five-line method may be easy to analyze.

A large application with complex business rules requires considerably more context.

Why Use AI for Code Review?

Developers already use code review to catch problems before code reaches production.

AI can help by providing an additional perspective before or during that process.

For example:

Without AI

Write code → Run tests → Create pull request → Wait for review → Fix issues

With AI-assisted review

Write code → Ask AI for initial review → Fix obvious issues → Run tests → Create pull request → Human review

The AI review does not replace the human review.

It can help improve the quality of the code before another developer spends time reviewing it.

If you are new to AI-assisted development, our article on 10 practical ways developers can use AI for coding provides a broader introduction.

10 Practical Ways AI Can Help With Code Review

1. Identify Potential Bugs

One of the simplest uses of AI code review is asking it to look for possible defects.

Example

Prompt:

Review the following Java method for potential bugs.

Check for:

• Incorrect logic
• Null handling
• Boundary conditions
• Incorrect assumptions
• Exception handling
• Unexpected inputs

Do not rewrite the code yet. First explain each potential issue and why it could cause a problem.

[Paste code]

This approach is useful because you are asking AI to explain the problem before changing the code.

That makes it easier to evaluate whether the suggestion is actually valid.

2. Review Error Handling

Poor error handling can make an application difficult to troubleshoot.

AI can review how code handles failures.

Example

Prompt:

Review this service method's error handling.

Identify:

• Exceptions that may not be handled
• Exceptions that are too broadly caught
• Errors that may be hidden
• Missing validation
• Cases where the application may return an incorrect response

Explain the potential impact of each issue.

This can be particularly useful in backend applications where multiple failure scenarios are possible.

3. Find Edge Cases

Code often works correctly for normal input but fails with unusual input.

AI can help brainstorm edge cases.

Example

Prompt:

Review this method and identify edge cases that may not be handled correctly.

Consider:

• Null values
• Empty values
• Very large values
• Boundary values
• Duplicate data
• Unexpected input
• Concurrent requests

For each case, explain what could happen.

The output can then be used to create additional tests.

This connects naturally with the workflow covered in AI-powered unit testing.

4. Check Code Readability

Code does not only need to work.

Other developers need to understand it.

AI can review:

• Variable names
• Method names
• Long methods
• Complex conditions
• Nested logic
• Comments
• Repeated patterns

Example

Prompt:

Review this code for readability and maintainability.

Identify:

• Poor variable or method names
• Overly complex logic
• Unnecessary nesting
• Repeated code
• Functions that may be doing too much

Explain which improvements would provide the most value.

Do not rewrite the entire method.

This is better than simply asking:

Make my code better.

The more specific request produces a more focused review.

5. Look for Security Problems

Security deserves special attention during code review.

Depending on the application, developers may need to consider:

• Input validation
• Authentication
• Authorization
• Injection risks
• Sensitive information exposure
• Insecure configuration
• Logging of confidential information
• Unsafe file handling
• Improper error messages

Example

Prompt:

Review this code specifically for potential security weaknesses.

Check for:

• Input validation problems
• Authorization issues
• Sensitive information exposure
• Injection risks
• Unsafe handling of external input
• Logging of sensitive data

For each finding, explain the risk and the relevant code section.

Do not assume a vulnerability exists unless the code provides evidence for it.

AI can help identify potential concerns, but security findings should be validated using appropriate security practices and tools.

Do not treat an AI response as proof that code is secure.

6. Identify Performance Problems

AI can also help developers think about performance.

For example, it may identify:

• Unnecessary loops
• Repeated database calls
• Inefficient data processing
• Expensive operations inside loops
• Excessive object creation
• Potential N+1 query patterns

Example

Prompt:

Review this code for potential performance problems.

Focus on:

• Algorithmic complexity
• Database access
• Repeated operations
• Large collections
• Unnecessary processing

Explain which issues are confirmed from the code and which are only potential concerns requiring profiling.

This distinction is important.

A code review can identify a possible performance problem, but actual performance should generally be measured.

7. Review Code Against Requirements

Code can be technically correct while still failing to meet the business requirement.

AI can compare implementation against clearly stated requirements.

Example

Prompt:

Here is the requirement:

[Paste requirement]

Here is the implementation:

[Paste code]

Review whether the implementation satisfies the requirement.

Identify:

• Requirements that are clearly satisfied
• Requirements that may not be satisfied
• Ambiguous areas
• Missing edge cases

Do not assume behavior that is not demonstrated by the code.

This can be particularly useful when requirements are detailed.

8. Review Changes Before a Pull Request

You can use AI as a pre-review step before submitting a pull request.

Example

Prompt:

Review this code change as a pre-pull-request review.

Focus only on issues that could affect:

• Correctness
• Security
• Reliability
• Maintainability
• Performance
• Testing

Rank findings as:

• High
• Medium
• Low

Explain why each finding matters.

Avoid stylistic suggestions unless they have meaningful maintainability value.

This can help reduce unnecessary noise.

The objective is not to generate the largest possible list of comments.

The objective is to find issues that actually matter.

9. Ask AI to Explain Existing Code

Code review is not always about finding bugs.

Sometimes the first challenge is understanding unfamiliar code.

You can ask AI to explain a method before reviewing it.

Example

Prompt:

Explain this code step by step.

Identify:

• Inputs
• Outputs
• External dependencies
• Important business rules
• Possible failure scenarios
• Side effects

Then summarize what the code appears to be designed to accomplish.

Once you understand the code, you can perform a more meaningful review.

10. Ask AI to Challenge Your Review

AI can also review your own review.

Suppose you identified three potential issues.

You can ask:

Example

Prompt:

I reviewed this code and identified the following concerns:

1. [Concern]
2. [Concern]
3. [Concern]

Review my findings against the code.

For each concern, tell me:

• Whether it is supported by the code
• What assumption I may be making
• What additional information would be needed
• Whether the issue appears high, medium, or low impact

Do not agree with me automatically.

This is a powerful use of AI because it encourages critical thinking rather than confirmation.

A Practical AI Code Review Workflow

A useful workflow is:

Understand → Review → Challenge → Test → Verify → Human Review

Step 1: Understand

Ask AI to explain unfamiliar code and its purpose.

Step 2: Review

Ask AI to look for specific categories of problems.

Step 3: Challenge

Ask AI to explain why each finding matters.

Step 4: Test

Create or update tests for relevant scenarios.

Step 5: Verify

Run the code, tests, static analysis, and other appropriate checks.

Step 6: Human Review

Have another developer review important changes.

This layered approach is much safer than asking AI:

Is my code correct?

and accepting the answer.

Example: Reviewing a Spring Boot Service

Consider a simple service method:

public User getUser(Long id) {
    return userRepository.findById(id).get();
}

A developer might ask:

Prompt:

Review this Spring Boot service method.

Check:

• Missing error handling
• Null or empty results
• API behavior
• Exception handling
• Maintainability

Explain the potential issues without rewriting the code.

AI may point out that directly calling get() on an empty Optional can result in an exception.

The developer can then decide how the application should actually handle the missing user.

The important point is that AI identified something worth investigating.

The developer still decides the correct behavior.

Don't Ask AI to Rewrite Everything

One common mistake is asking AI:

Rewrite this entire class and make it better.

This can produce a large amount of unnecessary change.

A better approach is:

Identify the three highest-impact problems first.

Then review those findings.

After you understand the issues, request targeted changes.

For example:

Update only the exception handling. Do not change the business logic, method signatures, or database behavior.

Smaller changes are usually easier to review and test.

AI Code Review and Testing Should Work Together

Code review and testing serve different purposes.

AI can help identify potential problems.

Tests can help verify actual behavior.

For example:

AI identifies:

What happens if the input list is empty?

You then create a test.

Test:

Given an empty list, the method should return an empty result rather than throw an exception.

Then run the test.

This creates a useful loop:

AI suggestion → Developer evaluation → Test → Result → Decision

Our article on AI-assisted unit testing for developers explores this testing workflow in more detail.

AI Code Review in a Pull Request Workflow

A practical development workflow might look like this:

Developer writes code

↓

Developer runs local tests

↓

AI performs an initial review

↓

Developer evaluates AI findings

↓

Developer fixes valid issues

↓

Tests are updated or added

↓

Automated checks run

↓

Pull request created

↓

Human reviewer examines the change

This gives AI a useful position in the development process without making it the final authority.

Common Mistakes When Using AI for Code Review

1. Assuming AI Is Always Correct

AI can misunderstand code and produce false positives.

Every finding should be evaluated.

2. Providing Too Little Context

A small code fragment may not contain enough information to understand the application's behavior.

When necessary, provide:

• Relevant interfaces
• Method contracts
• Requirements
• Important configuration
• Related classes

Do not provide sensitive information unnecessarily.

3. Asking for Too Many Things at Once

Instead of:

Find every bug, security issue, performance issue, style issue, architecture problem, and rewrite the code.

Break the review into focused stages.

4. Accepting Every Suggested Change

Not every AI suggestion improves the code.

Some changes may introduce unnecessary complexity.

5. Skipping Tests

An AI recommendation should not replace running the code.

6. Treating AI Review as a Security Audit

AI can help identify possible security problems, but important applications require appropriate security testing and expert review.

7. Sharing Confidential Code

Think carefully before sending proprietary source code or sensitive business information to an AI service.

How to Get Better AI Code Reviews

The quality of the review depends heavily on the quality of the request.

Instead of:

Review this code.

Try:

Review this Java service for correctness and reliability. Focus on null handling, exception handling, database behavior, concurrency risks, and edge cases. Explain each potential issue and its impact. Do not rewrite the code until I approve the findings.

This gives the AI a clear role.

You can also specify what not to review.

For example:

Ignore formatting and naming. Focus only on correctness and security.

This reduces unnecessary suggestions.

A Reusable AI Code Review Prompt

You can adapt this prompt for many projects:

Prompt:

Act as a senior software developer performing an initial code review.

Review the following code for:

1. Correctness
2. Error handling
3. Edge cases
4. Security
5. Performance
6. Maintainability
7. Testability

For each potential issue:

• Identify the relevant code
• Explain the problem
• Explain the possible impact
• State whether the issue is confirmed or requires additional context
• Suggest a focused improvement

Do not rewrite the entire code.

Do not invent requirements.

Do not assume a potential issue is definitely a bug without sufficient evidence.

When AI Code Review Is Especially Useful

AI-assisted review can be valuable when:

• You are working on repetitive code
• You are unfamiliar with part of a codebase
• You want a second perspective
• You want to identify edge cases
• You are preparing a pull request
• You want help understanding legacy code
• You are learning a new framework
• You want to improve test coverage

It can be especially useful before human review.

When You Should Be More Careful

Use additional caution when reviewing:

• Authentication systems
• Payment processing
• Security-sensitive code
• Infrastructure configuration
• Production database changes
• Healthcare-related applications
• Financial systems
• Critical business logic
• Large architectural changes

For these situations, AI can still provide assistance, but the review process should include appropriate human expertise and established testing or security practices.

Privacy and Security Considerations

Source code may contain sensitive information.

Before providing code to an AI system, consider whether it contains:

• API keys
• Passwords
• Access tokens
• Customer information
• Internal URLs
• Database credentials
• Proprietary algorithms
• Confidential business logic
• Private configuration

Remove secrets before sharing code.

Never use an AI review as a reason to expose credentials or other sensitive information.

A Beginner Workflow for AI Code Review

If you have never used AI for code review, start with a small method.

Step 1

Choose a non-sensitive piece of code.

Step 2

Ask AI to explain what the code does.

Step 3

Ask it to identify potential edge cases.

Step 4

Ask it to review error handling.

Step 5

Ask it to suggest relevant tests.

Step 6

Evaluate the suggestions yourself.

Step 7

Run the tests.

Step 8

Make only the changes that you agree are useful.

Step 9

Submit the code for normal human review.

This gives you experience without making AI responsible for your entire development process.

Final Thoughts

AI can be a useful additional reviewer for software developers.

It can help identify potential bugs, explain unfamiliar code, find edge cases, review error handling, highlight possible security concerns, and suggest areas for improvement.

But AI code review works best as part of a larger engineering process.

A reliable workflow combines:

AI assistance + automated testing + developer judgment + human code review

The goal is not to make AI responsible for deciding whether your code is good.

The goal is to use AI to ask better questions before your code reaches production.

When used this way, AI becomes another useful tool in the developer's toolkit rather than a replacement for engineering judgment.

Frequently Asked Questions

Can AI review my code?

Yes. AI tools can analyze source code and identify potential bugs, edge cases, readability issues, security concerns, and other problems. The quality of the review depends on the code and context provided.

Is AI code review reliable?

AI code review can be useful but is not guaranteed to be correct. AI can produce false positives, miss problems, or misunderstand business requirements.

Can AI replace human code reviewers?

For important software, AI should generally be treated as an additional review layer rather than a replacement for human developers.

Can AI find security vulnerabilities?

AI can identify potential security weaknesses, but important security findings should be validated using appropriate security tools, testing, and expert review.

Should I let AI rewrite code after finding an issue?

Not automatically. First understand and verify the finding. Then request a focused change rather than allowing AI to rewrite large sections unnecessarily.

Can AI review Spring Boot code?

Yes. AI can help review Spring Boot code for areas such as exception handling, validation, service logic, repository interactions, API behavior, and test coverage. The suggestions should still be validated against the application's actual requirements.

Should I provide my entire project to an AI tool?

Not necessarily. Start with the smallest amount of relevant context needed to perform the review. Be especially careful with proprietary or sensitive source code.

Can AI generate tests from code review findings?

Yes. Potential edge cases and bugs identified during review can be converted into test scenarios. The tests should then be reviewed and executed.

What is the best way to use AI during code review?

Use AI as an initial reviewer or second perspective. Ask focused questions, verify its findings, run tests, and keep human developers responsible for final decisions.

Conclusion

AI-assisted code review can make software development more efficient when used correctly.

You can use AI to:

• Find potential bugs
• Identify edge cases
• Review error handling
• Explore security concerns
• Analyze performance risks
• Improve readability
• Compare code with requirements
• Prepare pull requests
• Understand unfamiliar code
• Challenge your own review

But the safest approach is not:

AI reviews → AI decides → Code ships

Instead:

AI reviews → Developer evaluates → Tests verify → Human review → Code ships

That small difference matters.

AI is powerful when it helps developers think more deeply about their code.

It becomes risky when developers stop thinking about the code themselves.

Use AI as another pair of eyes, keep testing and human review in the process, and let engineering judgment remain in control.