Best practices for Salesforce static code analysis

Static code analysis is essential for Salesforce teams who want to build secure, high-quality apps that scale. Running static analysis gives feedback on code changes early in the development process, so teams can fix problems before they reach production. That means fewer bugs, less downtime, and stronger security.

This guide shares practical ways to get real value from static code analysis — from choosing the right tools to building a DevOps process that supports fast, safe, and collaborative delivery.

What is static code analysis?

Static code analysis (SCA) involves examining source code without executing it. It checks for coding errors, security vulnerabilities, performance concerns, and maintainability issues — helping teams spot problems early in development, before code is deployed or even run.

Unlike unit testing, which checks whether your code works as expected, static code analysis looks at how your code is written. It doesn’t rely on test cases or code coverage — instead, it scans the structure of your code and flags risks based on patterns and rules.

In Salesforce, static code analysis tools scan Apex code, classes, and triggers, Lightning Web Components (JavaScript and HTML), and Visualforce files. Some static analysis tools designed for Salesforce can also scan metadata — such as Flows, layouts, and permissions — to catch issues that aren’t visible in code alone. These tools ensure consistency across your org and reduce the risk of introducing issues that might be missed by unit tests alone.

Why static code analysis matters for Salesforce teams

Salesforce development often involves complex customizations, integrations, and automation. Even a small bug in Apex logic or a misconfigured metadata component can cause unexpected behavior or even outages in production. Static code analysis gives your team fast, automated feedback during development. Issues are caught early, fixes are easier, and technical debt is kept under control.

Static code analysis also plays a key role in supporting scalable DevOps processes and maintaining a healthy development environment as your team grows. With this kind of analysis embedded in your release pipeline, your team can maintain high standards and consistent code quality, even as your org grows in size and code complexity.

Choosing the right static code analysis tool

Before we look at the key best practices, it’s crucial to make sure your static code analysis tooling supports best practice approaches, fits your team’s needs now, and can scale with you as your org evolves.

To enable high-performing static code analysis for your team, look for a tool that offers:

• Programming language and metadata support: Support for Apex, Visualforce, Lightning Web Components (JavaScript and HTML), and the full range of Salesforce metadata. This breadth of coverage is crucial to catch vulnerabilities that could otherwise slip through in declarative metadata types, where many tools struggle to scan effectively.
• Customization options: The ability to define and adjust static code analysis rules based on your team’s coding standards and project needs helps to reduce noise and surface relevant issues.
• Integration with CI/CD pipelines: Seamless alignment with your deployment process, with automated code quality checks triggered by pull requests or pipeline stages speeds up your releases and minimizes the risk of human error.
• Scalability and performance: Capacity to handle complex orgs without sacrificing speed or accuracy, ensuring that code analysis stays fast, reliable, and effective as you scale.
• Integration with your DevOps process: Seamless integration into your DevOps process is key to avoiding bottlenecks and getting the most out of your static code analysis tool — supporting pull request reviews, quality gates, and CI/CD automation. While tools like PMD offer useful checks for Apex, they often require additional configuration to work effectively within a complete Salesforce DevOps pipeline.

Static code analysis best practices

Static code analysis can be the difference between a codebase that’s solid and scalable, and one that’s full of hidden problems. Done right, it helps your team catch issues early, write cleaner code, and build with confidence.

Many teams begin with open-source tools like PMD to check Apex against standard rulesets. While that’s a useful starting point, the real benefits of static code analysis come when it’s embedded across your CI/CD processes and tailored to the way your team works. Not just running final checks, but shaping how quality code gets written and shipped.

Below, we’ll walk through the best practices that take static analysis from good to great.

Define and enforce coding standards

To get real value from static code analysis and ensure maintainability across the entire codebase, set clear, consistent coding standards. A uniform coding style that’s enforceable makes it easier for developers to read and review each other’s code, and helps new team members get up to speed faster, without having to guess at code style or structure.

Static code analysis can help enforce your coding standards by building quality gates into your pipeline. These are automated checkpoints that make sure changes are compliant with your specified rulesets before they can be promoted — if the changes don’t comply with your specified rules, they get blocked so that nothing slips through. Quality gates keep feedback focused, prevent inconsistent code from slipping through, and help your team maintain a high standard as your org grows.

Configure tailored alerts

Once your coding standards are defined, configure your static code analysis rules to align with them, so that the alerts you receive are meaningful and relevant. Rulesets for best practice, security, and performance are a great starting point — but without customization, they can generate noisy alerts that aren’t truly relevant or actionable in your context, known as false positives. The real impact comes from reviewing and refining those defaults: disabling irrelevant rules, adjusting severity levels, and organising checks around your team’s coding conventions. This keeps alerts focused, actionable, and aligned with your org’s specific configuration.

Embed automated security checks

Security should be a fundamental part of every development workflow, not just a final step. Shift left and integrate security checks into your CI/CD pipeline to identify security flaws like hardcoded credentials or logic flaws that could lead to a data breach. Running static code analysis on every change reduces the risk of vulnerabilities slipping into production and strengthens security across every stage of your release process.

Tackle issues early to reduce technical debt

The longer issues go unresolved, the more costly and complex they become to fix. Static code analysis helps your team stay ahead by flagging potential problems early — from inconsistent logic and excessive DML operations to outdated metadata references. By regularly reviewing and addressing these issues as they appear, you reduce technical debt and maintain a healthier, more maintainable codebase.

Include performance and dependencies

Effective static code analysis doesn’t just scan individual lines of code, it also reveals how components interact and how well your codebase performs as a whole. That includes spotting performance bottlenecks in your code and problematic dependencies across metadata and configuration.

Inefficient logic, like nested loops or poorly structured SOQL queries, can hamper performance, introduce memory leaks, and increase the risk of hitting governor limits. At the same time, outdated packages, missing metadata, or incompatible versions can introduce bugs or cause deployment failures.

Catching these risks early results in tangible cost savings — from fewer deployment rollbacks and less time spent debugging performance issues or untangling broken dependencies.

Don’t leave out manual code reviews

Automation brings speed and consistency — but it’s even more powerful when paired with human insight. Manual reviews by experienced software developers add essential context — spotting subtle defects, design flaws, or architectural concerns. When automation handles the repetitive checks, developers are free to focus on more complex, high-value decisions that actually need human judgement.

Adopt best practices easily with Code reviews

Gearset’s Code reviews, powered by Clayton, is a static code analyzer created specifically for Salesforce. Code reviews automates debugging, checks against coding guidelines, and flags and fixes performance and security risks as you work.

Here’s how Code reviews helps you build best practices for static analysis into your workflow:

• Enforce coding standards: Code reviews lets you run automated checks based on rules tailored to each project’s coding standards. You can define custom rules in YAML, adjust severity, apply rules selectively across your org, and configure quality gates. You also get insight into how well those standards are being followed through rule coverage and trend reporting.
• Surface only meaningful alerts: Alerts from Code reviews appear directly at the pull request level — whether PRs are created in Gearset or externally (e.g., in GitHub, GitLab, Bitbucket). Because it’s designed for Salesforce, it avoids common false positive, surfacing only alerts that are genuinely relevant.
• Automate your security checks: Code reviews scans Apex, metadata, and configuration files for a wide range of security issues — from hardcoded secrets and insecure sharing settings. Using Static Application Security Testing (SAST), it identifies vulnerabilities like cross-site scripting (XSS), where malicious scripts can be injected into pages, and logic flaws that could let attackers bypass security checks or gain unauthorized access to data.
• Reduce technical debt: Code reviews monitors code health trends so you can measure and tackle existing technical debt as well as preventing more from building up.
• Easily integrate with your workflow: Code reviews runs automatically whenever a pull request is opened in a connected branch in your Git repository — whether the PR is created inside Gearset or directly in GitHub, GitLab, or Bitbucket. With early detection and autofix suggestions for common violations built into the workflow, developers can act on issues immediately.
• Spot performance problems and dependency issues: Code reviews analyzes not only Apex but also metadata like Flows and layouts. It highlights performance anti-patterns such as nested loops, inefficient SOQL, and excessive DML. It also surfaces dependency issues and metadata mismatches that could derail deployments.
• Free up reviewers for high-value feedback: Code reviews flags low-quality code in context, with suggested fixes developers can act on straight away, so reviewers can spend less time spotting problems and more time on higher-level feedback and architectural decisions.

Minimize your org vulnerabilities with static code analysis

Static code analysis is no longer optional for modern Salesforce development teams — it’s the key to ensuring code quality and robust org security. By embedding static code analysis best practices into your pipeline, avoiding common static code analysis mistakes, and adopting tooling for a consistent approach to code quality, you’ll catch issues earlier, reduce risk, and deliver more secure, maintainable applications.

Gearset’s Code reviews is built specifically for Salesforce teams — combining powerful automation with deep platform insight.

Start your free 30-day Gearset trial today and see how Code reviews can help you improve code quality, strengthen security, and streamline your development cycle.