Salesforce Code Review

Reviewing Apex well is a different job from writing it. The author has the context already; the reviewer has to reconstruct it from a diff, under time pressure, and is usually the same person who is also expected to ship something else today.

What reviewers actually need

A diff alone is close to the worst possible input. It shows what changed but not why, not what depends on it, and not what will be affected elsewhere in the org.

The minimum useful review package is: the request that produced the change, the code itself, the org context around the components being touched, the test evidence, and the author's stated reasoning. Remove any one of those and the review degrades into code style.

Code quality checks worth automating first

A meaningful share of review effort goes on things a machine does better and more consistently. None of these need judgement:

• Queries or callouts inside loops
• Missing null and empty-collection handling
• Hardcoded IDs, URLs and org-specific values
• Unused methods, variables and classes
• Missing test coverage for new branches
• Debug statements and commented-out code left behind
• Tabs, formatting and naming consistency

Removing these from human review is the single highest-leverage change available, because it converts reviewer attention from mechanical to architectural.

Org context is the part a diff cannot show

Most serious Salesforce defects are not visible in the changed code. They come from the interaction with what was already there: a trigger that already fires on the object, a validation rule that will reject the record being built, a field that already exists under a different name, a sharing setting that changes what the code can read at runtime.

This is why a general-purpose coding assistant gives incomplete review advice. It can critique the method in front of it; it cannot tell you that the object already has two automations writing the same field.

Dependencies and blast radius

Before approving a change, someone should be able to answer: what calls this, what does it call, and what breaks if the assumption behind it is wrong. In a mature org that answer exists in the metadata and simply is not being looked at.

SympleOps scans objects, fields, Flows, triggers, validation rules and references so the dependencies around a change are part of the review rather than discovered during deployment.

Test coverage as review evidence

A coverage percentage is close to useless as review input, because it measures lines executed rather than behaviour verified. Tests that assert nothing meaningful still move the number.

What a reviewer needs is the specific behaviour claims: this handles bulk correctly, this rejects the invalid combination, this respects the sharing model. For the fuller treatment, see Salesforce Testing Automation.

Security considerations that belong in review

Salesforce security review is mostly about two questions, and both are easy to miss in a diff: does this respect what the running user is allowed to see, and does it expose anything that should not leave the org.

• Sharing model and with sharing versus without sharing
• Field-level security on any field the code reads or writes
• Callouts and where data is being sent
• Credential and secret handling, including anything committed to source
• Whether an integration user has broader access than the task requires

Where AI review helps, and where it must not

AI review is genuinely useful as a preparation step. It can summarise what a change does, surface dependencies, flag the mechanical issues above, and point at untested branches. It makes the human review faster and better informed.

What it must not do is approve. An approving reviewer is accepting accountability, and that is the part that cannot be delegated — which is why the approval step in a governed pipeline sits with a person and promotion to production needs an explicit human confirmation.

Where to go next

For the broader developer workflow, see AI for Salesforce Developers; for test evidence, Salesforce Testing Automation; for where review sits in the pipeline, Salesforce DevOps with AI.