Verify AI-Generated Code: A Junior Developer’s Checklist

Junior developers can verify AI-generated code by treating it as an untrusted draft: understand what it is meant to do, inspect every change, and check it with tests, static-analysis tools, and a manual run. Do not merge code you cannot explain; use the same review standards as code written by a person, with extra attention to incorrect assumptions, edge cases, security, and dependencies.

Why generated code needs review

Code generation tools produce plausible text, not guaranteed-correct programs. A snippet may look idiomatic while calling a nonexistent method, relying on an unstated library version, mishandling an empty input, or solving a slightly different problem from the one requested. It can also compile and still be wrong: a function might return the wrong result for ordinary inputs or fail only under a particular condition.

This is not a reason to reject generated code automatically. It is a reason to evaluate it with ordinary engineering practices. Tests and tools provide evidence, but none proves that code is correct in every possible situation. Your job is to reduce uncertainty until you can justify the change and its limits.

Start with the requirement and the diff

Before judging an implementation, write down what the code should do. Identify expected inputs, outputs, side effects, and failure behavior. Then compare the proposed code with that requirement rather than asking only whether it looks reasonable.

Read the full diff, including changed configuration, tests, documentation, and dependency files.
Trace the main path: where data enters, how it is transformed, and where results or errors go.
Check whether the code changes behavior beyond the task, such as altering defaults or swallowing exceptions.
Ask what assumptions it makes about input shape, ordering, permissions, network availability, or existing state.

For example, if a function is supposed to find a user by email, check whether the requirement says email matching is case-insensitive, whether missing users have a defined result, and whether duplicate addresses are possible. Those details determine whether a simple exact string comparison is sufficient.

Build a small test plan

Tests should follow the requirement, not merely reproduce the generated implementation. Start with the normal case, then consider boundaries, invalid inputs, and relevant failures. If the project already has a test suite, learn its conventions and run the focused tests before the full suite.

Run existing tests related to the changed code and note whether any fail before your change.
Add or adapt tests for the expected behavior, including boundary cases and error handling.
Run the relevant test command, then broader checks where practical, such as the full test suite or build.
Inspect failures rather than repeatedly changing code until the tests turn green.

Suppose a function returns the first item in a list that matches a condition. Test a matching item at the beginning and end, no match, an empty list, and multiple matches if the specification says which one should win. Tests are most useful when they distinguish correct behavior from a plausible but incorrect alternative.

Passing tests are evidence, not a guarantee. A test suite may not cover the relevant edge case, and tests can encode the same mistaken assumption as the implementation. Read the assertions and compare them with the intended behavior.

Use project tools, then run the code

Use the repository’s documented commands where possible. A compiler or interpreter catches some syntax and type problems; a linter and formatter catch common consistency issues; static analysis may flag suspicious patterns. These tools have different purposes, and a clean result does not establish that the logic is right.

Run the project’s formatter and linter if they are part of its workflow.
Run type checking, compilation, or a build where the language and project support it.
Run focused tests, then integration or end-to-end checks that exercise how the change interacts with the application.
Start the application or a local example and try realistic inputs, including a failure case.

Use the instructions in the project rather than guessing commands: scripts vary between repositories and languages. If a command fails because a dependency is missing, check the project’s setup documentation and lockfile before installing or upgrading packages. An unnecessary upgrade can introduce unrelated changes.

Review security and dependencies

Generated code deserves the same security review as any other contribution. Look for unsafe handling of user-controlled data, secrets written to logs, overly broad permissions, weak validation, and error messages that disclose sensitive details. For web applications, consider whether input is escaped or parameterized appropriately for the context; escaping for one context does not automatically make data safe in another.

Check new dependencies and configuration changes deliberately. Confirm that a package is actually needed, that the code uses the project’s established dependency-management process, and that versions are not being changed casually. Do not paste credentials, private customer data, or confidential source code into a tool unless your organization explicitly permits it. Follow the team’s rules for approved tools and data handling.

Common traps and how to respond

Several review habits can create false confidence. A polished explanation is not proof; generated code can describe behavior it does not implement. Likewise, a passing happy-path test does not cover malformed input, empty collections, race conditions, or failures that matter to the feature.

Accepting code you cannot explain: ask for an explanation of the unfamiliar parts, then verify that explanation against the source and documentation. Simplify or remove code that is unnecessary.
Trusting a fabricated API: check the installed library version and its actual documentation or existing usage in the repository.
Testing only the generated example: create independent cases from the requirement, including at least one case likely to expose a wrong assumption.
Making a large change at once: split it into smaller changes so tests and review can identify where a problem was introduced.
Ignoring warnings: understand the warning and decide whether it is relevant; do not silence it merely to make a command pass.

If you cannot determine whether a behavior is correct, ask a teammate or maintainer with a specific question and a minimal example. That is better than presenting uncertainty as verified work.

A practical review checklist

Before you submit or merge a change, make sure you can answer these questions:

Does the implementation meet the stated requirement, including error and boundary behavior?
Can I explain each changed function and why the chosen approach fits this codebase?
Did I inspect the whole diff and avoid unrelated changes?
Did the relevant tests, build, and project checks run, and do I understand their results?
Have I considered input validation, permissions, sensitive data, and new dependencies?
Are remaining limitations or untested areas clearly disclosed in the review?

Verification is a process, not a single command. A good review combines specification, code reading, tests, tools, and communication. For a junior developer, explaining the reasoning is part of the evidence: it shows not just that the code ran, but that you understand what was checked and what remains uncertain.

Frequently Asked Questions

Can AI-generated code be trusted if all tests pass?
No. Passing tests show that the code met the cases tested. Review whether those cases reflect the requirement, and inspect security, integration, and untested edge cases.

What should I check first in generated code?
Start with the requirement and full diff. Confirm what inputs and outputs are expected, trace the main logic, and look for changes or assumptions outside the requested task.

How do I test code when I do not know every edge case?
Ask what inputs can occur, what the code should do when data is empty or invalid, and how failures should behave. Use the specification, existing project tests, and a teammate’s guidance to identify likely boundaries.

Should I rewrite code I cannot understand?
First investigate unfamiliar APIs and ask for a focused explanation. If the approach remains unclear or unnecessarily complex, simplify it or replace it with an approach you can explain and test.

Do linters and static analysis prove code is safe?
No. They can detect some known patterns and mistakes, but they cannot establish that the feature meets its requirements or that every security issue is absent.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.

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