Build a CI/CD pipeline to automate code reviews and ensure code quality

What if your CI/CD pipeline could automatically reject code that lowers quality? Learn how to enforce standards and ship better code, faster.

#1about 2 minutes

Introduction to automating code reviews and quality checks

An overview of how to automate code reviews and integrate code quality checks into a CI/CD pipeline to save developer time.

#2about 4 minutes

Understanding the purpose and cost of manual code reviews

Manual code reviews are essential for enforcing standards and education but are time-consuming, expensive, and prone to human error.

#3about 5 minutes

The workflow of an automated code review process

Automated code reviews integrate with platforms like GitHub to provide fast, unbiased feedback directly within a pull request.

#4about 7 minutes

A practical demo of automated feedback on a pull request

A Python code example with common errors is submitted in a pull request to demonstrate how an automated tool identifies and annotates issues.

#5about 3 minutes

Fixing code issues and verifying the automated checks

The identified issues, such as a generic exception and unreachable code, are fixed and resubmitted to show a successful automated review.

#6about 4 minutes

Why you should continuously monitor your codebase quality

Consistently monitoring code quality is crucial for long-term maintainability, reducing bugs, and preventing issues like code duplication.

#7about 4 minutes

Using key metrics to measure overall code quality

Code quality can be quantified using metrics like violation counts, function length, cyclomatic complexity, and the percentage of duplicated code.

#8about 3 minutes

Integrating automated quality gates into your CI/CD pipeline

A CI/CD pipeline can be configured to automatically run code quality analysis and fail the build if the quality drops below a set baseline.

#9about 6 minutes

How to configure a GitHub Action for quality checks

A YAML configuration file for GitHub Actions allows you to define specific quality thresholds for metrics like defect rate and function complexity.

#10about 3 minutes

How to define and customize what 'better code' means

The definition of 'better code' combines community best practices with customizable thresholds for metrics like function length and complexity.

#11about 2 minutes

Comparing different static analysis tools and philosophies

Codiga differentiates itself by leveraging community-driven open-source analysis tools rather than proprietary rule sets used by tools like SonarQube.

#12about 4 minutes

The future of code quality with AI coding assistants

While AI assistants like GitHub Copilot are promising, they currently generate insecure code, highlighting the continued importance of automated quality tools.