One-Day Rescue

Initial Situation

CI/CD pipelines were running multiple times a day but were extremely slow due to repeated package installations and no caching strategy.

Problems

• Builds took 18–25 minutes on average
• Developers frequently re-ran failed jobs due to timeouts
• No caching for `node_modules`, dependencies, or Playwright binary downloads

Actions Taken

• Enabled dependency caching using Gitlab for `node_modules`
• Added a checksum-based cache key strategy to avoid stale caches
• Split steps into separate jobs to improve parallelism
• Implemented Cypress binary and Cypress caching for E2E stage

Result

CI time dropped. Build flakiness reduced, and devs regained confidence in the CI pipeline.

Initial Situation

The codebase had accumulated over 500 SonarQube issues, many of them marked as blockers or critical. Developers were ignoring the reports due to noise and lack of prioritization.

Problems

• Over 500 unresolved issues in SonarQube, including duplications and high-complexity functions
• Developers had no clarity on which issues mattered
• New merge requests often introduced new smells without being flagged

Actions Taken

• Customized SonarQube rules to match the team's coding standards and filtered out false positives
• Grouped issues by impact and severity, focusing first on blockers and critical code smells
• Refactored duplicated code and extracted shared utilities
• Enabled pull request decoration and quality gates to enforce clean code going forward

Result

Resolved 70% of critical issues in under a week. Developers now use SonarQube proactively to prevent regressions.

Initial Situation

Manual dependency management led to security vulnerabilities and high maintenance effort.

Problems

• No automated vulnerability scanning for packages
• Outdated dependencies increased security risks
• Time-consuming manual updates and patch management

Actions Taken

• Integrated Renovate into the GitLab CI/CD pipeline for automatic vulnerability detection and package updates
• Set up automated merge requests for security updates
• Regularly monitored and prioritized security reports

Result

Automated updates significantly reduced security risks and relieved the team from manual maintenance.

Initial Situation

The project lacked consistent test coverage, causing unstable releases and hidden bugs.

Problems

• Many critical modules had less than 70% test coverage
• No quality gates in place to prevent low coverage merging
• Increasing risk of bugs slipping into production

Actions Taken

• Introduced a test coverage quality gate in the CI/CD pipeline to enforce minimum coverage
• Optimized and refactored parts of the codebase below 70% coverage to improve testability
• Educated the team on best practices to write maintainable tests and meet coverage standards

Result

Failed merges due to low coverage were prevented, improving release stability and code quality over time.

Initial Situation

The system lacked observability and error transparency, making it hard to detect production issues or understand user frustration.

Problems

• No centralized logging or error tracking
• Inconsistent log formats and missing context in error reports
• Bugs were often only discovered through user complaints
• No structured way to collect user feedback on crashes or failed actions

Actions Taken

• Implemented centralized logging with structured JSON logs
• Integrated Sentry for real-time error tracking with full stack traces and user context
• Enabled user feedback collection in Sentry for critical errors
• Set up dashboards and alerts for backend and frontend exceptions
• Introduced trace IDs to correlate logs and Sentry events across services

Result

Error detection improved dramatically. Developers could trace issues in real-time and respond proactively. User feedback led to faster prioritization of painful bugs.

Initial Situation

The team relied on manual deployments using CLI scripts and copy-paste instructions. This led to errors, delays, and inconsistent environments.

Problems

• Manual deployment process prone to human error
• Production releases lacked approval workflows or rollback mechanisms
• High stress during release days due to fragile process

Actions Taken

• Implemented automated deployment pipelines for staging, preprod, and production using GitLab CI
• Introduced environment-specific configuration management
• Added manual approval step for production deployments
• Set up version tagging, changelog generation, and rollback support

Result

Deployment time reduced from 45 minutes to under 5. Releases became consistent, traceable, and stress-free. Production deploys are now gated and safer.