Practice : Static Code Analysis

Purpose and Strategic Importance

Static Code Analysis helps engineering teams detect issues early in the development process—before code is run, deployed, or merged. It automatically identifies bugs, security vulnerabilities, style violations, and code smells, improving quality, maintainability, and safety at scale.

This practice builds a foundation for consistent coding standards and safer, more resilient systems. It also fosters a feedback culture where tools support engineers in writing better code without slowing them down.

Description of the Practice

  • Static analysis examines source code without executing it, using rule-based or heuristic engines to catch potential issues.
  • Tools can identify performance bottlenecks, logic errors, unsafe patterns, and adherence to coding guidelines.
  • Results are surfaced within developer workflows—editors, pull requests, and CI pipelines.
  • Common tools include SonarQube, ESLint, PMD, Flake8, and Checkstyle, depending on language and stack.
  • Rulesets are often customisable and aligned to team conventions and industry standards (e.g. OWASP, MISRA, PSR-12).

How to Practise It (Playbook)

1. Getting Started

  • Choose a static analysis tool that fits your language, stack, and ecosystem.
  • Integrate it into local development environments (IDEs or CLI) and CI/CD pipelines.
  • Use default rules to start, then customise based on team preferences and known risks.
  • Ensure feedback is fast and actionable—highlight issues at the PR level where possible.

2. Scaling and Maturing

  • Maintain a shared configuration in version control and apply it consistently across repos.
  • Establish severity levels for findings (e.g. block build on critical violations).
  • Review rules quarterly to tune signal-to-noise ratio and address evolving architecture or priorities.
  • Include static analysis coverage in quality dashboards and delivery reviews.
  • Combine with security and dependency scanning for broader coverage.

3. Team Behaviours to Encourage

  • Treat static analysis feedback as a guide, not a punishment.
  • Fix issues as part of the development flow, not in isolated clean-up phases.
  • Encourage teams to contribute improvements to rulesets and configurations.
  • Use findings in retrospectives or quality reviews to improve shared understanding.

4. Watch Out For…

  • Overwhelming teams with too many rules or false positives.
  • Ignoring results due to alert fatigue or inconsistent enforcement.
  • Applying rules unequally across teams or services.
  • Allowing issues to accumulate in long-lived branches or legacy systems.

5. Signals of Success

  • Static analysis runs are fast, reliable, and part of every build or PR.
  • Developers treat tooling as helpful—not as a blocker.
  • Teams catch and fix defects earlier, reducing production issues and code review churn.
  • Rule violations trend down over time, and code quality metrics improve.
  • Teams use static analysis data in sprint planning and architectural discussions.