AI coding assistants can generate a polished function in seconds. Unfortunately, polished code is not always correct code.
Generated code may compile, pass a simple test, and appear ready to use while still containing faulty assumptions, missed edge cases, weak error handling, or security vulnerabilities. Debugging it requires a slightly different approach because you did not build the original logic yourself.
Understand the Intended Behavior
Start by reading the code before running it. Determine what the function is supposed to accomplish, what inputs it expects, and what output it should produce.
Compare those expectations with the actual application. Many failures occur because the AI assumed the wrong data structure, dependency version, API response, or runtime environment.
Once you understand the intended behavior, you can investigate the cause of the problem instead of chasing symptoms.
Test Unexpected Inputs
AI-generated code often handles the happy path successfully. The problems appear when it receives a null value, an empty array, malformed data, an unexpected type, or a boundary value.
Create tests for these conditions deliberately. Look for off-by-one errors, silent type conversions, ineffective exception handling, and fallback logic that hides failures.
Code does not need to crash to be wrong. It may execute successfully and return an incorrect result.
Review Security Separately
Functional testing will not uncover every security weakness. Examine how the code validates input, manages credentials, constructs database queries, handles permissions, and communicates with external services.
Static analyzers and security scanners should be part of the normal review process for AI-generated code. Readable syntax and helpful comments do not make an implementation secure.
Use AI to Generate Tests Carefully
AI can help create unit tests and suggest edge cases, but those tests also require human review. A model may generate tests that repeat the assumptions it made when writing the original function.
Effective tests should challenge the implementation. They should cover expected behavior, invalid input, failure conditions, and security boundaries.
Linters, type checkers, test coverage reports, and static analysis tools can provide additional evidence that the code works as intended.
Know When to Start Over
Repeatedly patching the same function may indicate that its foundation is flawed. If every correction exposes another problem, rewriting the code from a clear specification may be faster than continuing to debug it.
AI can still assist with the rewrite. Give it the actual data structures, explicit requirements, expected outputs, and important edge cases. Better context usually produces more dependable code.
AI-generated code can accelerate development, but speed does not remove the need for engineering judgment. Developers still need to understand the logic, verify the behavior, test the boundaries, and recognize when a rewrite is the better solution.
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