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Tag: GitHub Actions

CI/CD Pipelines for Large Teams: How to Keep Velocity Without Breaking the Build
Continuous integration (CI) and continuous delivery (CD) are essential for modern software teams, as there is now a need for fast feature delivery and high-velocity improvements. However, achieving high speed may be difficult without compromising reliability, as the process involves coordinating among development teams working on different features of the same application simultaneously, product complexity and possible bottlenecks. Nevertheless, even in this challenging case, proper practices and tools can help these teams succeed.

Definition and Purpose of CI/CD

Elaborating on the terms, CI refers to merging code changes into a shared repository where automated builds and tests validate each update. On the other hand, CD, besides these operations, also automates the releases of validated code to production or staging. CI/CD’s goal is to detect code-related issues, reduce manual efforts and deliver new features as quickly as possible.

Why Velocity Matters

Velocity, which measures how fast teams deliver working software, indicates their productivity. For example, high velocity rates show that teams deliver features and respond to customer needs fast. In contrast, low velocity highlights that a team needs to improve the pace of releases, bug fixes, or other items. Although velocity mainly concerns speed, it also demonstrates whether the tooling, processes and team collaboration are suitable and whether developers can work efficiently.

CI/CD-Related Challenges in Large Teams

As the teams expand, the complexity of CI/CD also increases. Thus, the most common issues are:
1. Merge/integration conflicts occur more frequently because more developers make more code commits.
2. Shared codebases can cause failures that will affect multiple teams.
3. Building and testing pipelines are critical for developers. If they are too slow, it reduces velocity.
4. As the requirements for speed are high, this may lead teams to cut corners to achieve them, which is risky in terms of production outcomes.
Building a Resilient CI/CD Infrastructure

Large teams need various approaches to pipelines and infrastructure to overcome the issues mentioned before. The following are examples of possible approaches.
1. Modular: it allows you to separate errors and facilitate troubleshooting. For example, to build, then lint, unit test, integration test and finally deploy.
2. Parallelisation: running tests and builds simultaneously to reduce overall pipeline time.
3. Scalability: using containers (for example, Docker) and cloud infrastructure (GitLab CI, CircleCI) to scale pipelines’ storage capacity on demand.
4. Pipeline as a code: storing pipeline configurations in version control (YAML, Jenkins) to improve transparency.
5. Environment management: using temporary environments for feature testing. This approach will help reduce conflicts and failures when building in real-life conditions.
Tips for Collaboration and Code Quality

There are tips for improving collaboration and code quality. For example, besides isolating changes, encourage peer review among developers and invest in testing in every stage (unit, integration, end-to-end). Moreover, require all pull requests to pass automated checks before merging and prevent unreviewed code from being released. If failures happen, analyse them without prejudice and learn from them.

Team communication also matters in large teams. For instance, developers should define what “done” means and ensure everyone knows how to escalate issues. For tracking pipeline health, use specialised dashboards (Datadog, Buildkite). Use email to receive notifications when builds fail. Lastly, continuously review and remove bottlenecks.

Toolkit
1. CI/CD platforms: Jenkins, GitHub Actions, GitLab CI, CircleCI, Azure DevOps
2. Infrastructure as code: Terraform, Ansible
3. Test orchestration: Cypress, Selenium Grid, Playwright
4. Monitoring/alerting: Prometheus, Sentry, PagerDuty
Conclusion

In conclusion, maintaining velocity and reliability is possible by investing in a scalable architecture, pipeline health, segmentation, testing and automation. Clear and compelling team communication, as well as reviewing bottlenecks, are necessary to build CI/CD pipelines as living systems.
July 30, 2025
Streamlining CI/CD: Building Efficient Pipelines With GitHub Actions for Modern DevOps

Today, speed, reliability and automation are critical from a software development point of view. Continuous integration and continuous delivery (CI/CD) form the backbone of modern DevOps practices, enabling faster and more frequent delivery of high-quality software to the teams. Among many such tools is GitHub Actions — a powerful, flexible and native CI/CD platform that is integrated directly within GitHub.

In this article, we will look at how to use GitHub Actions to enhance your CI/CD pipelines, reduce operational overhead and create an automation and collaboration culture.

Why GitHub Actions for CI/CD?

The reason GitHub Actions is distinguished from other CI/CD tools like Jenkins or CircleCI is the seamless integration with the GitHub platform. GitHub Actions allows developers to build their own workflows using yet another markup language (YAML) files stored in the repository. This, in turn, enables versioning the actual configuration of the pipeline and makes this a collaborative effort. This marketplace has enough prebuilt actions, which allows teams to easily assemble workflows for tasks such as linting, testing or deploying to cloud platforms, such as AWS, Azure or Google Cloud. GitHub Actions also supports matrix builds, parallelism and custom runners, making it flexible enough for projects, small to large.

For DevOps, GitHub Actions provides better management of pipelines as it ties together everything — code, workflows and automation — into one ecosystem. Less context-switching brings more transparency as team members can see the status of the pipeline right in GitHub. With event-driven pipelines activated on push, pull requests and scheduled jobs, the overall strategy of GitHub Actions allows for running the pipelines only when required, thereby making optimum use of resources.

Building a CI/CD Pipeline With GitHub Actions

This basic example is meant to teach you the powers of GitHub Actions, using a CI/CD pipeline for a Node.js app. This pipeline will test and then deploy the application onto some cloud service (say, AWS) once the code is pushed to the ‘main’ branch.

Step 1: Define the Workflow

A GitHub Actions workflow is defined in a .yml file located in .github/workflows/. Here is a simple example of a Node.js application:

This workflow triggers, pushes and pulls requests to the main branch, checks out the code, sets up Node.js, installs dependencies and runs tests.

Key Benefits of Using GitHub Actions for CI/CD

1. Speed and Simplicity

GitHub Actions eliminates the need to switch between tools. Developers can set up pipelines directly in their repositories, leading to faster onboarding and simpler maintenance.

2. Enhanced Collaboration

Actions are visible to all collaborators, enabling better transparency. Developers can trigger tests from pull requests and receive immediate feedback, fostering a test-driven culture.

3. Scalability

Whether you are running simple unit tests or deploying microservices to Kubernetes, GitHub Actions scales with your needs. You can define matrix builds, parallel jobs and conditional steps.

4. Security

GitHub provides built-in security features like encrypted secrets, artifact signing and dependency scanning, enhancing the safety of your CI/CD process.

5. Cost Efficiency

For many projects, GitHub Actions is free. Even for enterprise-scale pipelines, it is a cost-effective alternative compared to managing self-hosted solutions.

Advanced Use Cases

1. Deploying to Cloud: Integrate with AWS, Azure or Google Cloud Platform (GCP) using official GitHub Actions:

2. Infrastructure as Code (IaC): Combine GitHub Actions with Terraform or Pulumi to deploy infrastructure:

3. Docker Build and Push: Automate containerization and deployment:

Conclusion

GitHub Actions is transforming CI/CD for teams by providing the best-in-class automation with GitHub integration. From the one-man show to a mature DevOps team, GitHub Actions streamlines your pipeline to more reliable deployments, shorter cycles and a better workflow.

It is time to maximize the potential of GitHub Actions — transforming manual steps into automated workflows, embracing modern DevOps practices and delivering software confidently.

May 19, 2025
GitHub Action Compromise Risks Data Leaks for 23,000 Repositories

A popular GitHub Action used in more than 23,000 code repositories has been compromised in a supply chain attack by attackers who introduced a malicious commit aimed at leaking secrets like passwords held in public repositories.

In the compromise, which is being tracked as CVE-2025-30066, bad actors modified the code in GitHub Actions tj-actions/changed-files – which is used by repositories to track change files – by injecting a Node.js function that includes base64-encoded instructions that download a malicious Python script that scans the memory of GitHub Runner, which runs jobs from a GitHub Actions workflow.

GitHub Runner’s memory holds passwords and other credentials used in the continuous integration and continuous delivery (CI/CD) pipeline.

“The compromised Action prints CI/CD secrets in GitHub Actions build logs. If the workflow logs are publicly accessible (such as in public repositories), anyone could potentially read these logs and obtain exposed secrets,” Varun Sharma, co-founder and CEO of startup StepSecurity, wrote in a report.

The Compromise is Found

StepSecurity was the first to uncover the compromise, which Sharma said started early March 14.

Cybersecurity firm Sysdig noted that after the Node.js function is inserted into the action, the base64 payload decodes to show a script that downloads additional Python code from a Github gist and the Python script identifies the GitHub Runner’s, opens the memory mappings, and searches for credentials using regular expressions.

“The results are output and stored within the GitHub Action build logs,” Sysdig threat researcher Michael Clark wrote. “The attackers must have access to these logs to extract the secrets.”

Code Repositories as Targets

This was the latest attack on a code repository, with GitHub and others – such as the Python Package Index (PyPI) and npm – becoming popular targets of threat actors, who put malicious code into developer packages that is then spread downstream when companies unknowingly install it.

It’s that wide reach that makes sources of software used across large numbers for applications systems the most desirable attack surfaces, according to Mitch Ashley, vice president and practice lead for DevOps and application development at The Futurum Group.

“These attacks on open source, repositories, libraries, package managers and container images are why DevSecOps has progressed from application security to software supply chain security,” Ashley told DevOps.

In the attack on GitHub Actions, the “code repository that monitors file changes served as the payload delivery system for nefarious code that read memory and data, sending that information back to the attackers,” Ashley said. “By pulling and building infected software from the repo and executing infected code, it opened that user’s information to the attackers.”

A Lot of Compromise, Some Leaks

According to StepSecurity’s Sharma, most of the versions of tj-actions/changed-files were compromised, adding a number of public repositories leaked secrets in build logs. GitHub took quick actions when the problem was detected, including shutting down the tj-actions/changed-files Action. According to Sharma, the repository later on March 15 was restored, with none of the versions including the malicious exploit code.

Threat intelligence researchers are still sorting out how the campaign worked or who was behind it, though tj-actions maintainers wrote that it appears it was the result of a compromised GitHub personal access token (PAT) that was used by @tj-actions-bot, a bot that had privileged access to the repository.

Repairs Underway

The maintainers said that after the problem was detected, the compromised PAT was revoked, the password for the tj-actions-bot account was updated, and access to the account now only uses the minimum necessary permissions. In addition, now requires a passkey.

Researchers with cybersecurity startup Wiz wrote that the primary risk of the compromise is to public repositories, where the secrets are dumped into workflow logs are also public, adding that “the risk to private repos is limited.”

Threat hunting by Wiz researchers identified dozens of repositories affected by the malicious GitHub Action, including repositories run by large organizations.

“In these repositories, the malicious payload successfully executed and caused secrets to leak in workflow logs,” they wrote. “Some of the leaked secrets we’ve identified so far include valid AWS access keys, GitHub Personal Access Tokens (PATs), npm pokens, private RSA Keys and more.”

More Than Secrets

Dimitri Stiliadis, co-founder and CTO at Endor Labs, said that the bad actor had bigger goals than simply accessing secrets.

“The attacker was likely not looking for secrets in public repositories — they are already public,” Dimitri Stiliadis, co-founder and CTO at Endor Labs, wrote in a blog post outlining the risks. “They were likely looking to compromise the software supply chain for other open source libraries, binaries, and artifacts created with this. Any public repository that creates packages or containers as part of a CI pipeline could have been impacted. That means potentially 1,000s of open source packages have the potential to have been compromised.”

The security vendors identified a range of actions organizations should take, including running a code search across repositories, reviewing logs for recent executions of the GitHub Action, and rotating secrets in GitHub Actions workflow run logs. They also should consider alternatives to GitHub Action.

March 17, 2025
Curtailing Software Automation Tool License Costs by Curbing License Needs

Many IT organizations currently utilize licensed automation tools, but providing licenses to all QA and development personnel is impractical. Typically, licenses are allocated to designated automation engineers who create and run tests as needed. This creates a challenge in allowing any team member to execute tests as required. With most automation engineers located offshore, additional licenses would be needed for onshore test execution, which can be expensive. Furthermore, ensuring high code quality, adequate coverage, absence of code smells and thorough security scans is crucial. Configuring a CI pipeline to invoke automation tests can help reduce license costs. CI tools sit in front of automation test tools, eliminating the need for individual licenses. In this article, I demonstrate this using GitHub Actions (GHA) as the CI tool. GHA is open source but incurs costs based on runner usage. I propose an innovative solution to eliminate runner costs too. Additionally, integrating code quality tools like SonarQube and CodeQL into the pipeline ensures security scans and highlights issues before deployment.

The major advantages of implementing a CI pipeline are:

No Automation Tool User Access is Required for Invoking Tests

Automation tools that require licenses can only be used by licensed individuals. However, when integrated with a CI pipeline, any user with CI access can initiate jobs without needing a direct license. This approach optimizes the management of the license pool, reduces costs and extends the tool’s utility for test automation. For instance, all developers with GHA access can now invoke the automation tool without requiring individual licenses.

Eliminating the Requirement for QA Resources to Constantly Oversee UI Test Execution on Their Desktops

During UI test execution, some automation tools prevent testers/developers from navigating away from the UI screen, blocking them from performing other tasks on their computers. By utilizing the GHA pipeline to invoke the automation tool, this restriction is removed, allowing developers and testers to work on other tasks while the automation runs in the background. After execution, an email with a detailed, user-friendly PDF test results report is sent.

Before the CI Pipeline came into the picture, the manual test execution setup had effectively reduced our hours from XXXX to XX, but we still needed to allocate XX hours because a QA resource is needed to monitor the screen during test execution.

However, an automated CI/CD integration reduces these XX hours to zero, as the CI pipeline manages sessions, eliminating the need for manual monitoring during test execution.

Other Benefits of Using GHA as a CI/CD Tool for Automation Tests

Access to GHA Free With User GitHub Account

GHA is a GitHub feature, which is enabled by default and hence no explicit access is required for GHA — it supports CI/CD workflows directly into the repositories.

Cost Savings: Eliminating AWS Infrastructure Cost

For a CI/CD pipeline, we must have a runner (virtual machine for running CI jobs) integrated, which is typically provisioned in the cloud, such as AWS incurring additional cost that grows as the load increases. However, with this solution, we are utilizing the organization’s Win365 machines as the runner and eliminating additional infrastructure runner cost incurred while using cloud services. All automation engineers are provided with Win365 machines for installing and executing automation scripts.

Replacing AWS Runners with Win365 runners

Automation team members for installing automation tools if provided with a virtual machine, which mirrors your desktop and incurs fixed charges. QA resources use this cloud instance only during business hours, leaving it free for the remaining 16 hours daily. By leveraging VMs as CI pipeline runners during this free time, we can significantly reduce AWS usage, eliminating the need to spin up new EC2 instances.

The below diagram depicts cost savings — here we are incurring a fixed VM monthly cost of $55, hence the pipeline using these VMs will incur zero runner cost. But if a new AWS instance were being used as a runner, we could have incurred an $80 monthly cost.

Cost Savings Calculation

Firewall Constraints and Automation tool Configurations

With GitHub being managed internally within the organizational network, we can avoid any security system issues as well as automation tool API permission issues, which is usually a challenge with externally hosted CI/CD tools.

Code Quality Checks Implementation

In the CI pipeline, we can implement CodeQL and SonarQube code quality tools avoiding any separate need for testing. CodeQL is a heavy tool and takes time for the pipeline to be executed. So, it is always recommended to trigger CodeQL in a separate pipeline while the results can be viewed within GitHub itself.

What is CodeQL

Post running the pipeline, errors if any highlighted by CodeQL can be viewed.

November 22, 2024
Riveting Repositories: JFrog GitHub Unification Leaps Onward

Repositories rarely rivet and rope themselves together quite like the union that has been forming between JFrog and GitHub. The company known for its bullfrog-themed universal repository manager for software application artifacts spanning dependencies, binaries and configuration files continues to forge a partnership with the widely popular web-based developer code collaboration platform.

The suggestion from users is that the two platforms coming together and forming an increasing number of integration points helps software supply chain security by displaying source-based security findings from GitHub alongside binary-based security findings from JFrog under GitHub’s Security tab.

Vulnerability Remediation

Developments now see the organizations releasing two updates that deepen the connection between the platforms. JFrog’s platform now integrates with GitHub Advanced Security, enabling GitHub Copilot Autofix to remediate vulnerabilities detected by JFrog SAST automatically.

GitHub Copilot Autofix analyzes vulnerabilities in software code and explains why they matter so that developers get a more rounded contextual understanding of what is happening. It then offers code suggestions that help software engineers fix vulnerabilities as they are found. JFrog SAST takes its name from Static Application Security Testing, a type of application security testing that scans applications in a “static state” (i.e. where no additional code or data input is occurring) to detect input validation errors, insecure dependencies, bad coding practices and other vulnerabilities that are intrinsic to the code itself.

In addition, JFrog Runtime Security findings have now been added to the JFrog Job Summary page in GitHub Actions.

¿Hablo Python, Java, JavaScript & Go?

JFrog’s Advanced Security offers static application security testing coverage, identifying a range of vulnerabilities across multiple languages such as Python, Java, JavaScript and Go. This analysis, combined with targeted remediation advice, helps developers address and rectify security flaws.

How does this affect development teams?

According to the JFrog blogging team, “A recent IDC report suggests that over two-thirds of developers agree that their security-related responsibilities require them to switch contexts frequently. This context-switching can significantly impact productivity and potentially lead to security oversights.”

Integration, Identification, Remediation

Now, JFrog’s SAST capabilities integrate with GitHub Copilot Autofix, to bring security fixes directly into the developer’s workflow. This allows developers to address issues across multiple languages as noted. JFrog identifies “problematic code” in pull requests, flagging security vulnerabilities to Copilot Autofix, which then uses this information to generate specific fix suggestions, showing developers what code changes are needed.

Copilot Autofix also provides developers with the ability to automatically generate new pull requests with suggested fixes for security issues found in the existing codebase. Software engineers can review these changes and merge them. Each suggestion includes an explanation of the issue and the reasoning behind the fix, improving developer security awareness and keeping them in control of the process.

By integrating the JFrog Platform’s advanced SAST capabilities directly into the GitHub workflow, the two organizations say that they’re making it easier for developers to write secure code because they no longer need to switch contexts between the development environment and security tools.

“Beyond DevOps and DevSecOps practices, the future will require advanced interactions with AI tools. Chatting with GitHub’s Copilot to select the right and secure software package based on the extensive metadata stored in the JFrog Catalog can be a game-changer. This integration will significantly enhance the efficiency of Copilot users across the software supply chain; binary-focused and code environments. This partnership offers the best of both worlds,” said John Nuttall, director of technology at AT&T.

Real-Time Runtime Reality

The goal for all developer teams is of course to identify and remediate vulnerabilities and accurately track all runtime components. Because runtime security is crucial for protecting applications in production environments, where the most challenging and potentially damaging threats often emerge, JFrog says its Runtime Security tool is a real-time Kubernetes monitoring solution that protects applications during execution and addresses core security challenges.

JFrog Runtime’s integration brings real-time production monitoring data directly into a GitHub workflow. After a build is completed in GitHub Actions, a link to the JFrog Runtime Live assessment dashboard is automatically added to the JFrog Job Summary page. This creates a new “Runtime Monitoring” section, filtered for the specific component built into that action. Developers and security teams can move from their GitHub workflow to a detailed view confirming the integrity and lineage of their build and deployment in one mouse click.

By combining JFrog’s security insights with GitHub’s development platform, the two organizations aim to help solidify and verify application integrity in production, so that software engineering teams can maintain a continuous security posture from code commit to the point of live production deployment.

October 29, 2024
Microsoft GitHub Alignment Fuels Birth of the AI Developer

Microsoft wants developers to evolve into AI developers. It’s a predictable enough progression i.e. first, developers were mainframe developers, then they became PC developers, mobile developers and then cloud developers. Next, logically, software engineers will have to become automation-centric AI developers with closer proximity to the models and engines that enable our new intelligence services.

To facilitate this shift, Redmond is of course championing the use of Microsoft Azure AI services as a route to building custom AI applications. To achieve the critical mass it so dearly seeks, Microsoft is partnering with GitHub to enable its 100 million+ developers to build AI applications directly on the GitHub platform.

Although Microsoft acquired GitHub in 2018 and the platform has subsequently been bolstered by CI/CD pipeline tools and wider security capabilities, it remains a comparatively independent entity in the style of a Red Hat inside IBM.

GitHub Models Marketplace

Microsoft has now made Azure AI Studio model selection functions available to developers through GitHub Models, a “marketplace” portal where software engineers who want to develop a generative AI application can find and experiment with AI models for free before considering token-based payment once an application moves to production. Efforts from Microsoft here also include the provision of APIs designed to enable production-ready AI applications.

GitHub Marketplace itself exists as a “free playground” where programmers can adjust model parameters and submit prompts to see how an AI model responds. This work also features integrations with Codespaces and Microsoft Visual Studio Code. GitHub Codespaces provides a secure development environment with a selection of prebuilt built-in resources that enjoy native integration with the GitHub platform.

GitHub Models came about in August of this year as a means of enabling the rise of the AI engineer with access to leading large and small language models.

The Rise Of The AI Engineer

“As AI model innovation accelerates, Azure remains committed to delivering the leading model selection and greatest model diversity to meet the unique cost, latency, design and safety needs of AI developers. Today, we offer the largest and most complete model library in the market, including the latest models from OpenAI, Meta, Mistral and Cohere and updates to our own Phi-3 family of small language models,” notes Asha Sharma, corporate vice president of AI platform, Microsoft.

With GitHub Models, Sharma suggests developers can explore the latest models along with other AI innovations such as next-generation “frontier models” i.e. those models that rank as large scale with many parameters and complex algorithmic logic such as Google Gemini, Anthropic’s Claude, AlphaFold’s DeepMind and indeed GPT-4 from OpenAI.

“For most of us, learning to be a developer didn’t happen on a linear path in the classroom. It took [plenty of] practicing, playing around and learning through experimentation. The same is true today for AI models. In the new interactive model playground, students, hobbyists and startups can explore the most popular private and open models from Meta, Mistral, Azure OpenAI Service Microsoft and others with just a few clicks and keystrokes,” blogged GitHub CEO Thomas Dohmke.

Many Modes & Modals Of Models

This entire dovetailing operation has likely come about as a result of the sheer size and scope of AI model development today. Twinned with the need to straddle deployment attack surfaces spanning cloud, edge, general-purpose and perhaps more task- or industry-specific application and data service deployments, the complexity factor out there is dizzying. GitHub Models aims to open the door for developers to experiment with multiple models, simplifying model experimentation and selection across the Azure AI catalog so that engineers can compare models, parameters, and prompts.

By making Azure AI an open, modular platform, Microsoft’s Sharma says the company aims to help our customers rapidly go from idea to code to cloud. With Azure AI on GitHub, developers can use Codespaces to set up a prototype or use the Prompty extension to generate code with GitHub Models directly in Microsoft Visual Studio Code.

“Increased model selection gives developers the broadest range of options for the individual applications they are building. But each model naturally brings with it increased complexity. To counteract this, we’re making it incredibly easy for every developer to experiment with a range of models through the Azure AI model inference API. Using this single API, GitHub developers can now access a common set of capabilities to compare performance across a diverse set of foundational models in a uniform and consistent way, easily switching between models to compare performance without changing the underlying code,” said Sharma.

On the road ahead, Microsoft says it will expand its integration even further. Surprisingly specific about what this means, this is Redmond’s commitment to bringing Azure AI’s language, vision and multi-modal services to GitHub, along with additional Azure AI toolchain elements. Organizations with an existing Azure subscription can purchase GitHub products via self-service, directly through Microsoft sales or via third-party solution providers and can adjust the number of GitHub seats as needed.

August 5, 2024
How GitHub Actions Simplifies Your CI/CD Workflow

In today’s fast-paced world, being able to deploy applications quickly is essential for success. However, setting up a CI/CD pipeline can be a tedious and time-consuming task. Fortunately, GitHub Actions can help simplify and automate your workflow, making it easier than ever to get your applications up and running in no time. In this blog post, we’ll take a look at how to set up a React application with GitHub Actions and explore the benefits of using this tool for your CI/CD needs.

Continuous Integration and Continuous Deployment

Continuous integration (CI) and continuous deployment (CD) are essential components of any product development workflow. CI is the process of automatically building and testing code changes, while CD is the process of automatically deploying code changes to a production environment. Both CI and CD help to ensure that code changes are deployed quickly and efficiently, with minimal errors.

Setting up a CI/CD pipeline can be a tedious and time-consuming task, particularly if you are not familiar with the required tools and technologies. However, using GitHub Actions can help to simplify and automate your CI/CD workflow, making it easier and faster to deploy your code changes.

GitHub Actions is a powerful tool that can be used to automate many different tasks, including building, testing and deploying code changes. In addition, GitHub Actions can be used to trigger other actions in response to specific events, such as when a code change is pushed to a remote repository. This makes it an ideal tool for automating your CI/CD workflow.

There are many advantages to using GitHub Actions for CI/CD. First, it is easy to set up and use, even if you are not familiar with the underlying technology. Second, it integrates seamlessly with GitHub, making it easy to track and manage code changes. Finally, it is highly customizable, allowing you to tailor the automation process to meet your specific needs.

Setting up a React Application

React is a popular JavaScript library for building user interfaces. Setting up a React application is not complicated, but there are a few important steps you need to follow.

1. Choose your development environment. You can use create-react-app to set up a development environment with all the necessary dependencies and scripts.
2. Install React and ReactDOM. React is the library that handles rendering of UI components while ReactDOM takes care of interaction with the DOM.
3. Create a component file. In this file, you will write your UI code using JSX syntax.
4. Include the component file in your HTML page. Now you can see your component rendered on the page!

Development Mode and Production Mode

In software development, there are two primary modes that code can be run in: development mode and production mode. Development mode is typically used by developers when they are actively working on code. In this mode, code is usually not optimized and may include debugging features that help developers identify and fix errors. Production mode, on the other hand, is used when code is ready to be deployed to end-users. In production mode, code is typically optimized for performance and security.

One of the benefits of using GitHub Actions is that it makes it easy to manage both development and production workflows from a single platform. With GitHub Actions, you can create separate workflows for development and production, or you can create a single workflow that automatically detects whether code is being pushed to a development or production branch. This flexibility can simplify your CI/CD pipeline and help you avoid errors that can occur when manually managing multiple workflow files.

To learn more about how to use GitHub Actions to simplify your CI/CD pipeline, check out the following resources:
– [GitHub Actions documentation](https://help.github.com/en/articles/about-github-actions)
– [Tutorial: Use GitHub Actions to deploy a static website](https://help.github.com/en/articles/configuring-a-workflow#creating-a-workflow-file)
– [CI/CD with GitHub Actions blog post](https://blog.github.com/2019-08-08-introducing-github-actions/)

Setting up GitHub Actions

Setting up GitHub Actions is simple and only requires a few steps.

First, you’ll need to create a new file in your project’s root directory called “.github/workflows/ci.yml”. You can name this file whatever you like, but for the purposes of this article we’ll call it “ci.yml”.
In this file, you’ll need to specify the language your project is written in and the name of the action you want to run. For example, if your project is written in JavaScript and you want to run the “lint” action, your file would look like this:

name: CI
on: [push]
jobs:
build:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v2
– name: Use Node.js ${{ matrix.node-version }}
uses: actions/setup-node@v1
with:
node-version: ${{ matrix.node-version }}

Breakdown of the Actions File

In the “ci.yml” file, there are three main sections: “name”, “on”, and “jobs”. The “name” section is where you specify the name of your CI workflow. The “on” section is where you specify the events that will trigger your workflow to run. In this example, the workflow will run every time code is pushed to any branch. The “jobs” section is where you define the actual jobs that make up your workflow. In this example, there is only one job called “build”.

The “build” job has two steps. The first step checks out the code from GitHub. The second step runs a Node.js linter on the code to check for syntax errors. If any errors are found, they will be displayed in the GitHub UI.

You can also add additional jobs to your workflow, such as testing or deployment jobs. For example, you could add a job that automatically deploys your code to a staging environment whenever code is pushed to the development branch. This would allow you to test new features in a safe environment before deploying them to production.

Adding additional jobs can help simplify your CI/CD pipeline by automating more of the process. This can free up time for other tasks, such as writing documentation or working on other features.

Running the Action

Assuming you have already set up a workflow file (ci.yml) and added a job to it, running the workflow is simple. All you need to do is push some code to your GitHub repository and the workflow will automatically run.

You can also manually run a workflow by going to the Actions tab of your GitHub repository and clicking the “Run Workflow” button. This can be useful for debugging purposes or if you want to run a particular job without pushing code.

If you want to see the output of a workflow, you can go to the Jobs tab and click on the job that you want to see. This will show you the log output of the job as it ran.

March 2, 2023
GitHub Previews Free CI/CD Migration Tool Due in 2023

At its GitHub Universe event, GitHub today unveiled a bevy of updates to its cloud service, including a forthcoming set of migration tools. The tools will automate migrations from legacy continuous integration/continuous delivery (CI/CD) platform to GitHub Actions.

GitHub Actions Importer is already being used by a small group of enterprise IT organizations to migrate workflows and template the GitHub CI/CD platform.

Damian Brady, senior manager for developer advocacy at GitHub, told conference attendees that these free tools will become more broadly available in 2023. GitHub Actions Importer will be distributed as a Docker container that manifests itself as an extension of the GitHub command line interface (CLI).

One of the biggest challenges the company has faced in driving adoption of its platforms is that many DevOps teams don’t want to rebuild the templates and workflows they are already using on their existing CI/CD platform, he noted.

Other GitHub Actions extensions include native integration with software development tools for building internet-of-things (IoT) applications and support for personal access tokens (PATs) that enable more granular control over permissions and repository access.

Other security capabilities include expanded access to audit log events in the enterprise edition of GitHub, an ability for maintainers of open source repositories to privately share vulnerability reports and support for Ruby as one of the programming languages now supported by a CodeQL tool for discovering vulnerabilities.

Additional extensions to existing services for developers also were previewed, including an ability to interact with the GitHub Copilot tool for automating code writing using voice commands. Dubbed “Hey, GitHub!” the tool is available as an experiment being advanced by the GitHub Next research team.

Finally, the company previewed enhancements to code search-and-view capabilities in the Codespaces tool that will now be made available for free for up to 60 hours per month to developers.

GitHub claims its CI/CD platform is now being used to process 10 million builds a day, most of which are employing code accessed via its repository. It’s not clear how that compares to other CI/CD platforms, but it’s apparent the GitHub Actions CI/CD platform has gained a significant amount of traction as GitHub continues to make a case for creating tighter integrations between its repository and CI/CD platform. GitHub is also clearly signaling it will spend a lot more time wooing organizations that already have a legacy CI/CD platform installed.

In the meantime, the next era of DevOps has arrived as workloads become both more complex to build and deploy across highly distributed computing environments. Many of the CI/CD platforms that organizations deployed are now straining under the weight of multiple customizations and extensions that have been made over the years. Each DevOps teams will need to decide whether maintaining those platforms makes the most technical and economic sense or if they should migrate to a more modern platform.

Regardless of what they decide, one thing that is certain is that DevOps teams won’t be getting larger, so the need to invest in higher levels of automation has become pressing.

November 9, 2022
Octopus Deploy Tightens Integration With GitHub Actions

Octopus Deploy announced today it has tightened the integration between its automated deployment platform and GitHub Actions continuous integration/continuous deployment (CI/CD) platform.

Harsh Sabikhi, senior vice president of revenue for Octopus Deploy, said GitHub Actions for Octopus Deploy v2 adds support for a push-build-information-action capability GitHub added to its platform that enables DevOps teams to track commits, builds and issues. That information is now also synchronized with a DevOps Insights tool from Octopus Deploy that surfaces metrics based on the DevOps Research and Assessment (DORA) metrics defined by Google.

Other capabilities added to the integration include support for semantic versioning release tags within workflows, job summaries and a revamped user interface that makes GitHub easier to access from within Octopus Deploy.

Sabikhi said that while GitHub Actions provides some deployment capabilities, the automation platform created by Octopus Deploy automates application release management across multiple clouds and on-premises IT environments. GitHub Actions is optimized for managing CI processes within the context of a Git-based workflow emanating from a cloud service managed by GitHub, he added.

The integration with GitHub Actions makes it simpler to extend those Git-based workflows across multiple deployment platforms, noted Sabikhi.

GitOps is, essentially, a more opinionated approach to DevOps that relies on an instance of the open source Git repository as the single source of truth about the environment rather than a set of application or server configuration files. GitHub views GitOps as a means of extending a code repository into the realm of DevOps workflows using the GitHub Actions platform.

As organizations embrace GitOps, however, many are finding it advantageous to more loosely couple CI and CD platforms. Integrated CI/CD platforms have been around now for more than a decade, but few organizations have been able to truly automate the delivery of applications using those platforms. Platforms such as Octopus Deploy have been created from the ground up to specifically manage deployments of applications. In addition to GitHub Actions, Octopus Deploy also provides integrations with multiple other CI platforms to enable DevOps teams to mix and match CI platforms as they best see fit, said Sabikhi.

It’s not clear how many DevOps teams are willing to acquire a separate CD platform, but as applications become more challenging to build and deploy in the cloud-native era many organizations are revisiting the DevOps processes they used to build and deploy monolithic applications. The challenge is many of them have spent years defining workflows based on legacy CI/CD platforms, so there is a lot of existing inertia that would need to be overcome when adopting a dedicated CD platform.

It’s clear that CD is finally becoming more achievable using platforms that automate much of the process. Each DevOps team will need to decide how many staff they want to dedicate specifically to managing those CD platforms; the pace at which applications are being deployed should accelerate as application release management becomes more automated.

November 2, 2022
Opsera Brings DevOps Intelligence to GitHub Actions

Opsera today announced it has extended its platform for collecting DevOps intelligence to include support for the GitHub Actions continuous integration/continuous delivery (CI/CD) platform.

Opsera CEO Kumar Chivukula said as GitHub Actions is adopted more widely, there is a growing need to provide greater visibility into Git-based workflows in real-time. A GitHub Actions Insights module for the Opsera platform makes it possible to track key performance indicators (KPIs) across those workflows, he added.

The goal is to make it simpler to make data-driven decisions about how best to optimize those workflows by identifying bottlenecks, noted Chivukula.

The Opsera platform surfaces that intelligence by scanning all repositories and branches, including commit history, and generating alerts and notifications whenever KPI thresholds or compliance requirements are exceeded. DevOps teams can also use the platform to assess their overall DevOps maturity using a set of DevOps Research and Assessment (DORA) metrics defined by Google. Going forward, Opsera also plans to start surfacing metrics based on the Supply chain Levels for Software Artifacts (SLSA) framework for securing software supply chains also defined by Google.

Platforms such as Opsera make it possible to apply analytics to DevOps workflows as they become more complex. The number of dependencies that exist between various application modules, for example, makes it exceedingly difficult for DevOps teams to manually identify the impact a delay in one project might have on any number of other downstream projects.

In addition, many organizations are keen to discover what best practices should be adopted by other application development teams.

Finally, many developers tend to use a repository as a dumping ground for all kinds of software artifacts, noted Chivukula. DevOps teams need an intelligence tool that makes it possible to identify which of those artifacts are the most essential to the business, he added. Otherwise, manually combing through a repository looking for artifacts becomes the DevOps equivalent of looking for a needle in a haystack, noted Chivukula.

GitOps is essentially a more opinionated approach to DevOps that relies on an instance of the open source Git repository as the single source of truth about the environment rather than a set of application or server configuration files. GitHub views GitOps as a means of extending a code repository into the realm of DevOps workflows using the GitHub Actions platform. It’s not clear how much traction GitHub Actions is gaining at the expense of other CI/CD platforms, but for organizations that are relatively new to DevOps and already using GitHub as a code repository, it presents a path of least resistance.

Regardless of why organizations adopt a DevOps intelligence platform, the need to optimize workflow across toolchains is becoming more pressing during an economic downturn. It’s becoming more clear that the cost of an application delivery delay directly impacts the level of revenue an organization generates in the age of digital business transformation.

November 2, 2022