A Comprehensive Guide to Speeding Up Salesforce Code Reviews with AI

Code reviews are good for quality, but harder than you think in a Salesforce environment.

Several factors make the review process slower:

Salesforce development often includes different components such as:

Reviewers often need to move between these different layers, which slows down the process.

A small change in a trigger or flow can impact important business processes like:

Because of this, reviewers must carefully check every modification.

Files such as metadata configurations and test classes can contain large XML structures. Reviewing these files line by line requires time and attention.

Senior developers frequently repeat the same feedback related to:

These repetitive checks consume valuable time.

In many teams that use GitHub or Azure DevOps the entire review process is manual. Even if you write checklists and documentation, most of the work is still about looking for common pitfalls.

This is where AI begins to play an important role.

AI is already helping with many parts of software development such as writing code, creating documentation and even recommending improvements.

Now the same technology is helping improve code reviews.

AI-powered tools look at pull requests and give you feedback in seconds. No more having to wait for a reviewer to pick up on every little detail. You get suggestions immediately after you push your changes.

Some of the common things AI tools can identify include:

Because of this early feedback, developers can fix many issues before a human reviewer even looks at the pull request.

AI reviews also help bring consistency to the process. Human reviews may vary depending on workload or availability, but AI tools apply the same standards every time.

Another advantage is that many tools provide explanations with their comments. This means that developers are able to fix bugs quicker, and to code better in the process.

Obviously AI can’t be perfect. It sometimes will tell you code is wrong when it’s technically correct or misinterpret logic that isn’t there for business reasons. However, when combined with human review, it works like an additional reviewer that performs the first pass automatically.

A variety of tools now help development teams improve their review process.

Some platforms are focused around code quality and static analysis. For example, SonarQube is looking for bugs, security vulnerabilities and maintenability leaks in your code.

And in the GitHub ecosystem, you have tools like GitHub Copilot which can provide suggestions and summaries to help developers understand pull requests.

Some of the many more modern AI-based review tools in use today include:

Pull Requests are scanned automatically by these platforms for security, quality, performance or code complexity issues.

Because a lot of them have free tiers or free trials, teams are able to try packages and pick the ones that make sense.

The insights shared here are based on practical experience using these tools during Salesforce projects and are not part of any sponsorship or promotion.

Among the tools tested, Qodo and CodeScene proved to be particularly useful.

Qodo works with your GitHub workflow and automatically scans pull requests with Apex, Lightning Web Components or SOQL queries, detecting problems with readability, risky patterns, and other potential improvements.

With proper configuration, it can also detect Salesforce-specific problems such as missing null checks. In many cases, it acts like an AI co-reviewer that gives early suggestions before the final human review.

CodeScene suits Azure DevOps teams The tool analyzes trend patterns of development and which areas suffer from growing technical debt instead of a single pull request.

By tracking code changes over time, it helps teams understand which modules are becoming harder to maintain.

For teams that are interested in trying out AI code reviews, Qodo has a free plan to enable easy sign-up.

The setup process usually involves a few simple steps,

After installation, GitHub shows a confirmation message and the integration becomes active.From that moment onward, Qodo automatically analyzes pull requests in the selected repositories and adds review comments when necessary.

When you use AI tools in the development cycle the process becomes much easier.

This is a normal process, it looks something like this:

Sometimes the tool also highlights things like duplicated code, complex methods, or missing null checks. If a new developer tries to make changes to the code, they can instantly fix these minor issues.

once a senior engineer reviews the PR, most of these minor issues have already been fixed.

There are many benefits to AI assisted reviews for development teams.

They get faster feedback, and can fix problems earlier in the development process. They get consistent feedback, because the rules used to review pull requests are the same for every pull request. So another advantage is that senior developers have less time for fixing the same basic problems and more time for the architecture and business logic.

But it’s not perfect. Even correct code could be called an issue, it’s going to need adjustment to conform to a team’s coding conventions, and more than anything it doesn’t see the contextual business framing. This means there will almost always be a need for a human reviewer.

That’s why the best solution is a dual approach. The robot does the structure checks and all repetitive work while the human is concentrating on more detailed design and logical decisions.

AI code reviews help developers, they don’t replace them.

CodeScene and Qodo in Salesforce projects can help to cut down on review latency and add consistency across teams.

One practical starting point for these tools is to run AI reviews in parallel with manual reviews. Over time, teams frequently find they’ll make fewer duplicate comments, approve pull-requests more quickly, and spend more time actually creating useful features.

Code reviews will always remain an important part of Salesforce development. The difference now is that AI allows teams to perform them faster and with less friction, helping developers spend more time delivering solutions that create real business value.