How to Review AI-Generated Code for Bugs Before You Run It

Imagine asking an AI coding assistant to prepare a small public project update from your task records. It returns a Python function in seconds, complete with readable variable names and a short explanation. At first glance, the code appears to do exactly what you requested.

Then you notice something easy to miss. The condition that selects public records also allows certain private records through. The returned objects include a field containing internal notes. If the function feeds a sharing or publishing workflow, that mistake could expose information you never intended to distribute.

Nothing in the snippet needs to throw an exception for the result to be harmful. A successful execution may be precisely what makes the mistake consequential. Once data has been uploaded, emailed, or published, finding the problem afterward may be too late to prevent disclosure.

This is why learning to review AI-generated code before execution matters. You are not merely looking for syntax errors. You are checking whether the proposed implementation matches the request, respects data boundaries, and avoids operations that were never authorized.

Codex, Claude Code, GitHub Copilot, Cursor, Qodo, and CodeRabbit provide different ways to inspect code changes and identify potential problems. Some can review local changes, while others specialize in pull requests. Their findings are useful evidence, but they do not replace your responsibility to understand the intended behavior.

We will begin with a practical pre-run review process, inspect a flawed Python function without executing its unsafe version, and examine a separate JavaScript example involving asynchronous operations. Then we will look at where each review tool fits and how to decide whether a proposed change is ready for testing.

Understand what to review before running AI code

Code review and debugging answer different questions. Debugging usually begins with a failure you have observed. Reviewing newly generated code begins before you know whether the implementation will behave correctly in your environment.

You examine the proposed changes against a set of expectations and constraints. The objective is to recognize problems that can be identified from the code, its surrounding context, and the intended behavior before granting it access to real systems.

Define the task the code was actually authorized to perform

Start with the original request. If you asked an assistant to format a collection of records for display, did you also authorize it to save a file, install a dependency, or send information to an external service?

AI coding agents may propose additional work because it seems convenient or necessary to finish the task. A broader solution is not always a better one. Every additional operation should be justified by the user's actual goal.

Write the intended behavior in ordinary language before reviewing the code. For a public project update, the requirement might be to include only active records explicitly approved for sharing. The output should contain a title and status, not private notes or internal contact information.

That statement creates a standard against which the code can be judged. Without it, a review assistant may praise a function for collecting all relevant records while overlooking the fact that some of those records must remain private.

Identify the data and permissions the code can reach

A script that performs arithmetic on supplied numbers has a smaller impact than one that reads private documents, updates a database, or sends messages. The same coding error can have very different consequences depending on the system surrounding it.

Look for file operations, network clients, database connections, access tokens, environment variables, and functions that trigger external actions. These are not automatically problems. They tell you where a wrong assumption could have effects beyond the function.

Check whether the requested task requires each capability. A report preview does not normally need write access to a production database. A filename generator does not need to delete existing files. A code review request does not need to deploy an application.

Permissions also matter when an agent is allowed to run commands. Reading a source file and changing its contents are separate activities. A review-only task should not silently become an implementation or execution task.

Know which conclusions static review can support

Static review means examining code without executing the program being reviewed. It can reveal a condition that plainly violates a requirement, an unguarded destructive operation, or an external request to a destination you did not authorize.

It can also identify possible problems that require additional context. An unfamiliar function call might be safe because another component validates its arguments. A suspicious default might be an intentional part of the application's contract. The reviewer should distinguish confirmed defects from questions.

Static inspection cannot prove that a program works in every environment. It may not reveal behavior hidden inside dependencies, remote services, runtime configuration, or code that was not included in the review.

The purpose is therefore to reduce foreseeable risk before controlled verification begins. A clean review means no supported issue was found within the inspected scope. It does not mean that execution is guaranteed to be safe or correct.

Inspect diffs, dependencies, and changed behavior

When an AI coding agent modifies an existing project, inspect the change rather than reading only the final explanation. An assistant may describe the work as a small improvement while its proposed diff changes several files or alters a public interface.

A diff shows what was added, removed, and replaced. It helps you separate the intended change from unrelated modifications that require further explanation.

Start with the file list and review every meaningful change

Before reading individual lines, look at which files were changed. A request to correct a display label may reasonably affect a UI component and its related configuration. It deserves closer attention if the assistant also modifies authentication settings, environment files, or deployment scripts.

In a Git-based project, the status and diff commands can show local changes. For a review of uncommitted work, read-only Git inspection commands may help you identify the changed files and inspect the patch.

git status --short

git diff --stat

git diff --no-ext-diff

These commands are intended to inspect the working tree. The ordinary diff does not display every untracked file or every staged change, so examine those separately when relevant. A review that ignores newly created files may miss the part of a change that introduces the real risk.

Read the file list first, then inspect the changed functions and their callers. Pay particular attention to removed validation, renamed fields, altered return values, and changes that affect how data moves between components.

Look for changes to interfaces and data meaning

A function can remain syntactically valid while changing a contract that other code relies on. Renaming a response field, changing a default value, or returning a different data structure may break callers that were not included in the generated patch.

Suppose a function previously returned a list of objects. An AI assistant changes it to return a dictionary keyed by ID because that structure seems more efficient. The new implementation may be reasonable in isolation, but every caller expecting a list now needs attention.

The same concern applies to error behavior. A function that previously raised an exception might begin returning an empty object. That can make a calling process treat failure as legitimate empty data.

Ask which documented behaviors change and which consumers depend on them. A reviewer should not dismiss a compatibility change merely because the edited function appears simpler afterward.

Distinguish unnecessary complexity from necessary safeguards

AI-generated code can become more complicated than the task requires. The assistant might introduce several helpers, a configuration layer, and a new abstraction for a one-time data transformation.

Complexity matters because every additional branch and dependency gives the reviewer more behavior to understand. A larger design can also make future changes difficult for someone who did not participate in the original AI conversation.

Still, shorter code is not automatically safer. Validation and explicit permission checks may require more lines than an unsafe shortcut. The question is whether the added code has a clear responsibility.

Review abstractions in context. Can the same requirement be met with fewer moving parts without losing important safeguards? If a helper is necessary, does its name describe what it really does? Can an unfamiliar reader follow the data through it?

Find hidden bugs in an AI-generated Python function

Let us review a deliberately flawed Python function before allowing it to feed a public project dashboard. The intended task is simple: create a list of active project cards that have been explicitly marked public.

Each returned card should contain only a title and status. Records marked private must stay out of the output. Archived records should also be excluded, even when they were previously public.

This is an instructional example, not a real private dataset. We can determine several defects by reading the code and comparing it with those requirements.

Inspect the generated selection logic without executing it

Imagine an assistant proposing the following function. The comprehension is concise, and the variable names are familiar. That makes the code seem reassuring at a glance.

def public_cards(records):
    return [
        {
            "title": row["title"],
            "status": row["status"],
            "private_notes": row.get("private_notes", ""),
        }
        for row in records
        if (
            row.get("visibility") == "public"
            or row.get("status") == "active"
        )
    ]

The problem lies in the relationship between the selection condition and the output fields. We do not need to execute this function to see that its behavior differs from the stated contract.

The condition uses or. A record is included whenever it is public, even if it is archived. It is also included whenever it is active, even if its visibility is private.

The contract requires both properties to be true. A record must be explicitly public and active. The generated condition permits either property to be sufficient on its own.

A second problem appears in the returned object. It contains private_notes, even though the public card is supposed to contain only a title and status.

That field is not needed for the requested display. Including it creates a disclosure risk if the output is passed to an API, public page, or shared export without further filtering.

Record the findings with a concrete failure condition

A useful code review does not merely say that the function is "insecure" or that its filtering needs improvement. It explains exactly which input would expose the problem.

An active private record satisfies the generated condition because its status is active. A public archived record also satisfies the condition because its visibility is public.

Neither record belongs in the output. The incorrect result follows directly from the Boolean expression. This makes the finding more useful than a vague warning about possible filtering mistakes.

The first three findings are supported directly by the snippet and the stated requirements. The missing-field question needs an explicit decision about invalid input.

This separation matters. Review comments become more trustworthy when they distinguish demonstrated mistakes from assumptions that still need confirmation.

Apply a correction that enforces the public-data boundary

We will choose a strict but narrow input policy. Every row must be a dictionary. Only records explicitly marked public and active are eligible. An eligible record must have a non-empty string title.

The function will return a fresh dictionary containing only the two allowed fields. It will not include private notes, owner information, or unrelated data.

The corrected code below implements those decisions. The validation is visible without introducing a new package or changing unrelated parts of the project.

def public_cards(records):
    cards = []

    for position, row in enumerate(records, start=1):
        if not isinstance(row, dict):
            raise TypeError(
                f"Row {position} must be an object"
            )

        if (
            row.get("visibility") != "public"
            or row.get("status") != "active"
        ):
            continue

        title = row.get("title")

        if not isinstance(title, str) or not title.strip():
            raise ValueError(
                f"Row {position} needs a title"
            )

        cards.append({
            "title": title.strip(),
            "status": "active",
        })

    return cards

The eligibility condition now rejects a record whenever either requirement is not satisfied. In other words, only records that are both public and active can reach the output-building step.

The output is also deliberately restrictive. A new field added to the input later will not automatically become part of the public card. This is safer than copying the entire source record and trying to remove sensitive fields one by one.

That approach is often called allowlisting. You specify the fields permitted in an output rather than assuming that all fields are safe unless explicitly forbidden.

The corrected function operates only on an in-memory collection. It does not publish, email, or upload anything. That makes its data-selection responsibility easier to inspect separately from any later sharing operation.

The displayed correction was checked with artificial records in an isolated Python environment. The checks confirmed that a public active record produced the expected card, private and archived records were excluded, empty input returned an empty result, and an eligible record with a blank title raised ValueError.

Those checks support this small example. They do not establish that a real publishing service, API permission model, or external data source is safe. The review should address those components separately if they are part of your application.

Recognize security, data, and asynchronous risks

Some generated-code problems are visible in a single expression. Others appear only when you understand what the function can access or what its caller assumes.

A pre-execution review should therefore include more than logic. You also need to inspect permissions, external effects, error handling, and the timing of operations that complete asynchronously.

Inspect secret handling and external destinations

Code that reads an environment variable is not automatically unsafe. Many applications use environment variables to obtain configuration and credentials.

The important question is what happens to those values afterward. An access token should not be printed to ordinary logs, inserted into a public report, or sent to a destination unrelated to the authorized task.

Look carefully at network requests introduced by generated code. Confirm the destination, the data included in the request, and the reason the connection is needed.

A new telemetry call or analytics helper may appear harmless. If it sends raw records or credentials to an external endpoint, however, it changes the project's data boundaries and deserves explicit review.

Check logging statements for a similar problem. Error messages can accidentally include request bodies, private filenames, account details, or authentication headers. More detailed logging is not always safer when the underlying values are sensitive.

Review file operations and proposed shell commands

AI coding agents sometimes propose shell commands alongside source-code edits. They may install packages, remove generated files, change permissions, or update local configuration to make a workflow run.

Each command deserves attention before execution. A broad deletion command can remove files outside the intended temporary directory. A script that overwrites an output file may destroy an earlier result without asking.

Path handling also matters. If untrusted input is used directly to construct a filename, it may contain directory separators or other unexpected path elements. A code review should confirm that the destination remains within the intended location.

Installing dependencies creates another boundary. Check whether a new package is necessary, where it comes from, and whether the requested operation could be implemented using an existing trusted dependency.

Do not run an unfamiliar command with administrator privileges as a shortcut around a permission error. The permission failure may be protecting a resource the task should not modify.

Catch asynchronous success messages that arrive too early

JavaScript introduces another review concern: a function may report success before the operation it initiated has actually finished.

Suppose a settings screen calls an asynchronous storage function. The storage operation returns a Promise that settles when the save completes or fails.

An AI assistant proposes this wrapper:

async function saveSettings(settings, writeSettings) {
  writeSettings(settings);

  return { saved: true };
}

The wrapper calls writeSettings() without awaiting the returned Promise. It then returns a success object.

Under the stated contract, this is a real problem. The caller can receive a fulfilled result before the storage operation has completed. If the write later rejects, that failure is not represented by the wrapper's returned success.

Adding await makes the wrapper wait for the write operation before returning.

async function saveSettings(settings, writeSettings) {
  await writeSettings(settings);

  return { saved: true };
}

With this change, the wrapper resolves successfully only after the awaited operation resolves. A rejection from that operation propagates through the wrapper unless the surrounding code deliberately handles it.

The correction depends on the stated interface: writeSettings() must return a Promise that accurately represents the save operation. If it starts background work and returns before that work completes, adding await alone cannot guarantee durable storage.

This distinction should appear in the review. The goal is not to claim that a keyword solves every asynchronous problem. It is to verify that success is reported at the correct point in the operation's actual contract.

Compare Codex, Claude Code, Copilot, Cursor, Qodo, and CodeRabbit

The six tools discussed here can assist with reviewing code, but they operate in different contexts. A local coding agent can inspect a working directory before you open a pull request. A repository review service may analyze changes after they are submitted for review.

Those workflows should not be confused with a promise that no code will execute. A tool may run analysis commands or use additional services depending on its configuration. If you need a strictly non-executing inspection, verify its permissions and selected review mode.

Use Codex and Claude Code for context-aware review

Codex can review code changes and identify potential bugs, behavioral risks, and important compatibility concerns. Its code review workflow can also use repository-specific guidance to focus on project rules.

OpenAI's documentation describes using AGENTS.md to communicate review expectations that would otherwise remain informal. For example, a repository might require all public exports to exclude internal notes.

That instruction helps a reviewer understand the project's intended data boundary. It does not replace ordinary security controls or testing, and reviewers should still evaluate the actual code.

Claude Code can also inspect changes and support review of a local project. Anthropic separately documents a GitHub pull-request Code Review service for eligible Team and Enterprise organizations.

That service analyzes changes with multiple review agents and reports findings on relevant lines. It does not automatically approve or block the pull request. Claude Code also offers local review workflows distinct from the organization service.

Use GitHub Copilot for reviews within the pull request lifecycle

GitHub Copilot code review works within GitHub's pull-request workflow. Depending on the available configuration, reviews can be requested manually or triggered automatically for relevant pull requests.

This makes it useful when you already organize changes through branches and pull requests. The feedback appears alongside the code under review, where you can inspect the finding and its surrounding context.

GitHub also supports repository guidance that can make review comments more relevant to local conventions. The precise options and availability depend on your plan and repository settings.

A Copilot review should be treated like other review feedback. A finding is something to investigate, not an instruction that must be applied without checking it.

Likewise, the absence of a finding does not show that every changed path is correct. Confirm what was reviewed and whether the comments correspond to the latest proposed code.

Use Cursor Bugbot to inspect proposed pull request changes

Cursor offers Bugbot for AI-assisted review of pull requests. Its documented workflow examines changes and provides findings about potential bugs, security issues, and code quality.

Bugbot can operate through pull-request integrations, with configuration determining when reviews run and which repositories are included.

That is useful when the main review artifact is a pull request, rather than a standalone code snippet in a chat.

A review comment still needs to explain the actual failure condition. Check whether the identified problem follows from the proposed code and the application's contract.

If the finding refers to behavior outside the diff, inspect the related source before deciding that the reported issue is confirmed.

Use Qodo and CodeRabbit for repository-oriented review

Qodo provides context-aware review workflows focused on code changes, requirements, and affected repository behavior. Its documentation describes reviewing pull requests with specialized analysis and reporting findings that can be discussed or addressed.

This is particularly relevant when a proposed change touches a shared interface or differs from an established requirement. The reviewer needs more than a description of the edited lines to assess its broader impact.

CodeRabbit also supports AI-assisted code review through pull-request workflows. Its platform includes review findings, repository guidance, and tools for examining changes and their risks.

Both products can help organize review feedback, but the responsibility to confirm an issue remains with the person or team accepting the proposed change.

None of these tools eliminates the need to understand what the program is allowed to do. A tool that examines a pull request may provide broader repository context than a pasted snippet. A local reviewer may be easier to use before a pull request exists.

Choose the workflow that matches where your code lives, which changes need inspection, and what permissions you are willing to grant. Review tools should support your decision, not replace it.

Use a structured pre-run AI code review checklist

You do not need an elaborate review process for every short function. A consistent sequence of questions can already uncover common mistakes before they reach a real environment.

The aim is to make the decision traceable. You should be able to explain what the code is intended to accomplish, which risks were examined, and why the proposed changes were accepted.

Review the change in a fixed order

Begin with intent, then move to the changed behavior and possible effects. This prevents the review from becoming a collection of superficial formatting comments.

Syntax, naming, and style matter for maintainability, but they should not distract from a condition that exposes private data or a command that could overwrite important files.

Give the review assistant a specific reporting format

A broad prompt such as "review my code" may produce a mixture of style advice, speculative concerns, and useful defects. It can be hard to tell which findings genuinely matter.

A more focused request tells the assistant which behavior is required, what it may inspect, and how each finding should be supported.

Ask for findings before general praise or summary. Each confirmed issue should identify the relevant code and explain the condition under which it produces an incorrect or unsafe result.

This prompt can be adapted for Codex, Claude Code, ChatGPT, or another assistant capable of examining supplied source code. In an agentic environment, text instructions should be accompanied by appropriate permission settings.

A request not to execute code is useful, but it is not a substitute for configuring the tool to operate within the intended authority.

Prioritize findings by impact and supporting evidence

A code review becomes difficult to use when twenty minor suggestions obscure one serious data-disclosure bug. Group findings by their effect on the program, not merely by how easy they are to fix.

A finding deserves high priority when the code can perform an unauthorized action, expose sensitive information, corrupt important data, or violate a critical requirement under a supported condition.

A possible performance problem may also matter, but the reviewer should identify the workload or input that would trigger it. Do not present a vague concern as an established failure without relevant evidence.

Do not count review comments as a quality score. One precise finding that prevents disclosure can be more valuable than many cosmetic observations.

Also avoid accepting every automated suggestion. A reviewer may misunderstand an intentional interface or overlook a constraint outside the visible code. Confirm each proposed correction against the actual requirement.

Decide whether to accept, revise, or reject a change

The final result of a pre-run review should be a clear decision about the proposed code. It should not end with a vague statement that the implementation "looks good."

A useful decision identifies whether the change satisfies the defined contract, whether consequential risks remain, and what the next authorized action should be.

Accept only changes whose behavior and authority are understood

A change may be ready for controlled testing when the reviewed code matches the intended behavior and no unresolved high-impact finding prevents that step.

Acceptance at this stage does not mean approval for unrestricted execution. A script that handles sensitive records may still require a restricted environment and additional checks before being connected to real data.

For a small local function, the next action may be a controlled execution with fabricated inputs. For a database migration, external integration, or production change, the next step may require a separate review and deployment plan.

The key is to keep authorization proportional to the possible effect of the code.

Request narrow corrections instead of a new uncontrolled rewrite

When you identify a defect, describe the smallest change that would address it. If the issue is an incorrect Boolean condition, changing the condition may be sufficient.

If a public output includes private fields, the correction should define and enforce the permitted output schema. There is no need to redesign the entire application unless the surrounding structure also prevents the requirement from being met.

Ask the assistant to explain every additional change it proposes. A correction that introduces new dependencies, changes unrelated behavior, or expands permissions deserves another focused review.

Review the revised diff rather than assuming the assistant only changed the lines it described. A response stating that an issue has been fixed is not evidence that the final code now satisfies the requirement.

Hand off a clear review result before testing or deployment

A useful review handoff contains the intended change, the important findings, what was corrected, and what remains unverified.

Keep the statement factual. If the code was inspected but not executed, say that. If a safe example was run locally, identify the checked behavior without extending the result to unrelated systems.

Do not write "fully verified" when you have only read the diff. Likewise, do not treat a completed automated review as proof that production behavior is correct.

The handoff should tell the next person exactly which questions still need controlled execution or broader system testing.

Frequently Asked Questions

Conclusion: review the change before trusting the code

AI coding tools can produce useful code quickly, but readable source is not automatically correct source. A function may run successfully while selecting forbidden records, changing an established interface, or performing an operation that was never requested.

The public project card example showed how a small Boolean mistake could include private and archived records. Its returned data structure also contained information that should not have been made available to a sharing workflow.

Those problems were visible from the source code and the required behavior. We did not need to run the flawed implementation against real records to recognize them.

The JavaScript example revealed a different kind of risk. A function can report success before an asynchronous operation has finished. Reviewing the Promise contract is necessary to understand whether the result means what the caller expects.

Codex, Claude Code, GitHub Copilot, Cursor Bugbot, Qodo, and CodeRabbit can help with parts of this inspection. Their value comes from the supported findings they surface, not from replacing your understanding of the system.

Before running the next piece of AI-generated code, read the proposed change against its actual requirements. Identify the data it can reach, the operations it can perform, and the contracts it may alter.

If a problem is supported by the code, request a narrow correction and inspect the revised diff. If the important uncertainties are resolved, the code can move to an appropriate controlled verification stage.