AI Code Generation, Review & Debug: The Complete Guide

Most AI code tools begin by asking you to upload your source to someone else's servers. For anyone working under an audit mandate, in a regulated environment, or simply on a codebase they don't own the rights to distribute, that first step is where the conversation ends.

The Code Review workspace in NextGen GRC takes the opposite approach. The model runs in your browser, on your machine. There is no API key, no upload, no round trip. This guide covers every feature and how to use it.

Watch it work

A short walkthrough showing code generated, reviewed, and executed end to end.

Every feature at a glance

Generating code from a description

Above the review workspace sits ✨ Generate Code. Pick a target language, describe what you need in plain English, and it writes the implementation.

The distinction that matters is what "implementation" means. Most tools hand back a function and leave the surrounding work to you. Ask for something real here — say, an invoice service backed by a database with validation rules — and you get the structure that makes it usable: the domain model, input validation, error handling that returns a result rather than throwing, the data-access layer, and tests.

Then the part that separates a generator from a guess: the same workspace compiles and runs what it produced. Press ▶ Run Code under the output and you see it execute — real output, real errors, real behaviour.

Getting a better result

• Describe behaviour, not implementation. "Flag rows where sales exceed budget by more than 10%" produces better code than "write a for loop with an if statement".
• State the constraints you actually have — the database, the framework version, the error-handling convention your team uses. They change the output substantially.
• Include the edge cases you care about. Nulls, empty inputs, concurrent writes. Mentioned up front, they get handled; mentioned later, they become a rewrite.
• Switch to Code Pro for harder problems. The larger model is slower and noticeably better on anything with real structure to it.

Generated code lands in the same editor the review tools read from — so Generate → Run → Review → Security is one continuous loop rather than four separate tools.

Step-by-step: your first review

Running a security pass

Press 🔒 Security for a dedicated analysis. It looks for injection paths, unsafe deserialisation, missing authorisation checks, weak cryptography, unvalidated input reaching a sink, and secrets in source.

This is separate from Review by design. A general review optimises for readability and correctness; a security pass assumes an adversary and asks a different question of the same code.

Debugging with a stack trace

Press 🐛 Debug and a second box appears for the error. Paste the full stack trace — not just the message.

System.NullReferenceException: Object reference not set to an instance of an object.
   at Acme.Billing.InvoiceService.Calculate(Invoice inv) in InvoiceService.cs:line 47
   at Acme.Billing.BatchRunner.Process(Int32 batchId) in BatchRunner.cs:line 112

With both the trace and the source, it can identify which specific value is null and why that path was reachable — rather than listing everything that could theoretically cause a null reference.

Compile and run — proving it works

Both workspaces have a run panel: ▶ Run Code under generated output, ▶ Run & Test under the review editor. Both compile the code and show what actually happened — what it printed, what it returned, what it threw, and where.

This is the difference between a tool that discusses your code and one that engages with it. A review claiming a function returns the wrong value is an opinion until something runs it. A compiler error is not an opinion.

It also closes the loop on generation. Code that compiles and produces the expected output has cleared a bar that "looks correct" never does.

Screenshot to code (OCR)

Press 🖼 Screenshot and choose an image. The code is extracted and dropped into the editor, ready to review.

Code screenshots are not document scans, and treating them as such is why general-purpose OCR mangles them. Three things are handled specifically:

• Dark themes are inverted. OCR engines expect dark text on a light background. Most editors are the opposite, and an uninverted dark screenshot reads as noise.
• Line-number gutters are stripped — but only when a leading digit run appears on most lines, so a genuine 12 in your code survives.
• Indentation is preserved, and smart quotes and em-dashes are normalised back to ASCII. An OCR'd " will not compile.

Everything runs through a WebAssembly OCR engine served from the same domain as the page. The image is never uploaded — which matters when the screenshot is of a production system.

Language coverage

Detection and review work across:

ABAP and COBOL are deliberate. The systems running payroll, the general ledger and settlement at most large enterprises are written in them — and they are precisely the systems mainstream AI coding tools have no answer for. If your audit scope includes SAP, that gap is the whole problem.

Why on-device matters

Running the model locally is not a performance decision. It is what makes the tool usable at all in the environments this platform serves:

• No data-residency question. Nothing crosses a border because nothing leaves the browser.
• No third-party processor to add to your GDPR record or vendor risk assessment.
• Works air-gapped. Once loaded, no network is required.
• No API keys to provision, rotate, or leak.

For a team that cannot paste client code into a public tool, the difference is not a feature comparison. It is whether the tool can be used.