A guide to DevOps tools and DevOps automation toolchains

A DevOps toolchain is a core tenant of any DevOps practice, helping organizations apply automation to the SDLC and improve their ability to deliver higher-quality software faster. It’s also one of the more tangible aspects of DevOps.

Some organizations will invest in an “all-in-one” platform to build their DevOps toolchain. Others will integrate different best-of-breed solutions to create a toolchain. But critically, there is no one-size-fits-all approach to DevOps or building a DevOps toolchain.

In this guide, we’ll explore how the best DevOps toolchains address each stage of the SDLC. This includes:

• Planning and collaboration tools
• Build tools
• Continuous integration tools
• Continuous deployment tools
• Operations and continuous monitoring tools
• Security and DevSecOps tools

DevOps planning and collaboration tools

In large part, DevOps seeks to bring previously siloed teams together across all stages of the SDLC—and that starts at the planning stage. From chat applications to project management tools, there are a number of tools organizations can implement in their DevOps toolchains to better align and encourage collaboration in an organization during its planning stages.

DevOps planning and collaboration tools generally fall into two buckets:

• Product and roadmap planning: Having a centralized place to plan, track, and manage work is a foundational capability for any modern development team—and DevOps organizations, too. The best tools help organizations build plans, sprints, and roadmaps while being able to assign and track work from the initial plans to the delivered end product. Need an example? Try looking at our own public product roadmap plans, which we build using projects on GitHub.

• Team communication: Maintaining communication throughout the planning process is key to spurring collaboration—and having a preserved record of conversations that led to a given decision can be incredibly helpful. Tools such as GitHub Discussions, chat applications, and issue trackers that enable team conversations are key here. GitHub provides apps to help your team integrate with Slack or Microsoft Teams. The best tools will integrate with your project planning, too. That means you can turn a discussion into an executable piece of work, or turn an idea into a discussion if more conversation is needed before work can start.

DevOps build tools

Once developers commit code changes to a central repository, the build stage begins—and that means using version control to create shared repositories, provisioning development environments, and integrating code, among other things. At this stage, organizations typically leverage the following DevOps tools:

• Version and source control: A version control system is designed to automatically record file changes and preserve records of previous file versions, which enables code rollbacks, historical references, and multiple code branches allowing developers to collaboratively code and work in parallel.
  
  Platforms such as GitHub offer version control and source control with features such as pull requests, which enable individual developers to get reviews on proposed code changes before they are integrated into the main code branch. The best version and source control platforms integrate with your broader DevOps toolchain, and enable product teams to collaborate across the SDLC.

• Pre-production development environments: In a DevOps practice, developers need to leverage virtual environments that mirror production as closely as possible. These environments are identical to one another and easy to provision, so that all developers can quickly build and test code changes in consistent environments.
  
  Organizations will often leverage containerization platforms and registries such as GitHub Packages to build standardized, pre-production environments for development teams. Ideally, these platforms should integrate into the source control solution so that when a team member commits new code, it triggers the automated provisioning of a pre-production environment.

• Cloud-based integrated developer environments (IDE): Cloud-based IDEs offer comprehensive development environments that are pre-configured and can be quickly provisioned. These are an increasingly popular tool in DevSecOps (and development circles more broadly, for that matter) since they help standardize developer environments including security configurations across machines. And since they’re centrally managed, cloud-based IDEs also keep code off an individual developer’s computer, which can improve overall development security.
  
  Tools such as GitHub Codespaces also feature deep integrations into core DevOps platforms. This can improve development speeds by cutting down the amount of time it takes to spin up a developer environment—and reducing the need to wait for running builds and tests locally.

• Infrastructure as code: The rise in cloud infrastructure, or Infrastructure as a Service (IaaS), has made it simpler to quickly provision resources to meet real-time demand. It’s also introduced a need among organizations to manage complex cloud-based infrastructure at scale—both to provision new resources as they’re needed and to manage resource clusters for pre- and post-production environments.
  
  Infrastructure as code (IaC) draws on DevOps best practices to provision and manage cloud infrastructure resources right from a version control system like GitHub via YAML files. These files specify a CI/CD workflow automation that is triggered by an event such as a pull request, code commit, or code merge. Once this event happens, the workflow automates the provisioning and management of cloud infrastructure resources.
  
  Since IaC relies on a combination of YAML configuration files that are stored in a shared repository, it’s critical to make sure your version control and CI/CD platform of choice integrate seamlessly. Tools such as GitHub Actions offer this type of integration, which make it easier to manage infrastructure right from your repository with CI/CD.

DevOps continuous integration tools

Continuous integration (CI) is a mainstay of any DevOps practice and combines the cultural practice of frequent code commits with automation to integrate that code successfully and create builds.

To successfully adopt CI, DevOps organizations typically use tools and platforms that do three things:

• CI: As a practice, CI often involves committing multiple code changes a day to a shared repository and using automation to integrate these changes, applying a series of automated tests to the merged codebase to ensure its stability, and preparing the codebase for deployment. This level of automation requires deep integration between a version control solution and the larger CI/CD platform, which enables DevOps organizations to build CI/CD pipelines that are triggered by a code commit.
  
  When you’re looking for a good CI solution, you’ll want to make sure it easily integrates with your version control solution. This integration is key to making sure you can build an automated pipeline that starts as soon as your development teams commit code changes.
  
  A good example of this level of integration comes with the GitHub platform, which features platform-native CI/CD via GitHub Actions and also features a number of pre-built integrations for third-party CI/CD services. You’ll also want to make sure that whatever CI/CD platform you choose can automatically apply tests at all stages of the SDLC and includes native support for containerization platforms.

• Automated testing: Automated testing tools are a core part of any DevOps toolchain. Most platforms will offer automated testing as a capability making it simple to incorporate automated tests into key parts of the pipeline—for instance, after a code change is merged to the main branch.
  
  The goal is to have a comprehensive testing strategy with basic unit tests, integration tests, and acceptance tests that are applied at key points in the SDLC. The best testing tools integrate seamlessly with—or are part of—your CI/CD platform and offer built-in code coverage and testing visualization. You’ll also want to look for testing platforms that enable matrix build testing capabilities, or allow you to simultaneously test builds across multiple operating systems and runtime versions.
  
  It’s also a good practice to ensure that your automated test solution of choice comes with monitoring and alerts that integrate with your chat application of choice. This means that if something breaks, you can quickly get a notification and work to fix whatever the underlying problem. Tools such as GitHub Actions, for instance, can be used to send alerts to chat applications once a test fails for quicker remediation.

• Packaging: Once code changes clear all tests in a CI/CD pipeline, they are packaged into independent units of code and prepared for deployment. DevOps organizations will typically leverage a package manager like GitHub Packages to facilitate the delivery of software packages to a shared repository in preparation for a release.
  
  Package managers help remove the need for manual installations and help bundle code dependencies within a given project. There are different package managers for different code libraries—but you should ideally look for a solution that integrates with your version control system and your CI/CD platform.

DevOps continuous deployment tools

Continuous deployment builds upon CI/CD by removing the need for human intervention when releasing software. Instead, a continuous deployment practice applies automation to every stage of the SDLC. That means if a code change clears all automated tests, it is deployed to production.

DevOps organizations that adopt continuous deployment will typically use tools that fall into two categories:

• Automated deployment: Automated deployments are a core part of continuous deployment and having a toolchain that supports automated deployment. These capabilities are typically present in most CI/CD platforms. However, there is no one-size-fits-all approach to building out a continuous deployment pipeline—and it won’t work with every application or environment.
  
  If you decide to invest in continuous deployment, look for platforms that readily support the development and management of multiple environments. Importantly, you’ll want a solution that helps protect you from “server drift,” or differences between development, pre-production, and production environments. You’ll also want to consider a platform that supports blue-green deployments, which enables you to slowly migrate traffic from an old version of an application to a new release to ensure its stability in production.
  
  At GitHub, we provide deployment dashboards and CI/CD visualization displays as part of our native CI/CD tool GitHub Actions—and we consider these core features for any continuous deployment toolchain. This is meant to give DevOps organizations full visibility into different code branches, automated test results, audit logs, and ongoing deployments as they happen.

• Configuration management: Configuration management is a process where technology teams manage the different environmental configurations necessary in the core infrastructure and application systems across the life of the product. It’s also something that is frequently paired with CI/CD and versioning control via automation.
  
  Just as a CI/CD pipeline applies automation across the SDLC, configuration management tools automatically apply configuration changes in response to trigger-based events. These automated workflows are typically built in a CI/CD tool and stored as text files (like YAML) in a shared Git repository. These can be used to orchestrate and manage container clusters with platforms. They can also be used to manage infrastructure as code (IaC) practices. GitHub repositories and issues make it easy for IT professionals to work with systems that produce text-based configuration files for both IaC and Configuration as Code (CaC).

Continuous testing tools

In a DevOps practice, testing doesn’t stop at CI/CD—it’s an ongoing practice that extends throughout the SDLC. And more importantly, DevOps seeks to replace siloed QA teams with a continuous testing practice that leverages automation and holistic testing strategies across the SDLC.

Each DevOps organization will design its own continuous testing strategy in accordance with its needs. GitHub Actions provides workflow automation related to testing and supports a rich set of open source and commercial testing tools. Every continuous testing strategy will leverage a combination of the following test types across the SDLC:

• Unit testing: Unit tests are a way of testing small units of code to verify that they are structured correctly with isolated components. They are also the easiest tests to build and the fastest to execute, making them a foundational test to automate in any continuous testing practice.

• Integration testing: Once you commit code changes to a repository, integration tests ensure build stability and that the codebase continues to work successfully. These tests are used to identify defects that emerge when different application processes and code units are merged together. Integration tests are commonly automated to begin as soon as code changes are committed to a codebase and test the interplay of multiple parts of an application.

• End-to-end and regression testing: Building on integration testing, end-to-end and regression tests are applied after a codebase is packaged and staged in a pre-production environment. These tests are used to check if any old defects, bugs, or issues are reintroduced by code changes. Regression testing is commonly used before and after deployments to ensure that an application works as expected and does not contain any previously identified issues.

• Production testing: After an application is deployed, production-level tests monitor application health and stability—and identify any issues before they cause problems for end users. Importantly, these tests help organizations identify any potential problems in a production environment with live user traffic that can’t be fully replicated in a pre-production environment.

DevOps operations and continuous monitoring tools

A successful DevOps practice touches every stage of the SDLC—and that includes production-level software, too. This means companies need to invest in core operations and continuous monitoring tools to evaluate application and infrastructure performance. If done right, these tools can help continuously identify potential issues across the SDLC.

DevOps organizations will be best served by investing in tools that have the following capabilities:

• Application and infrastructure monitoring: Application and infrastructure monitoring are core components of a successful continuous monitoring practice. The best tools offer 24/7 automated monitoring of the application and infrastructure health and give DevOps practitioners alerts when something goes wrong—and visibility into what the underlying problem might be.
  
  Ideally, you’ll want to monitor application health in pre-production and production environments to track any process issues or areas to improve overall performance. This is also true for your underlying infrastructure where monitoring can lead to insights on how to improve your infrastructure as code (IaC) and configuration management policies.
  
  Try looking for a tool that integrates with your version control tool and chat applications so you can immediately send alerts to the right people, and create issues to outline the scope of work for a solution.

• Audit logs: Auditing is a central part of an effective operations and continuous monitoring practice—and resolving any incidents if and when they happen. They give DevOps practitioners a record of what happened, where it happened, and when it happened and can be critical to build behavioral models that led to an issue and improve application and infrastructure health.
  
  Look for DevOps tools that have live logs and auditing retention periods to equip your teams with the information they need to improve core services and application performance.

• Incident and change tracking: The primary goal of DevOps is to help organizations ship higher-quality software faster through deep collaboration and automation. And that means tracking incidents and changes as they arise and sharing them with the right people is critical.
  
  To build a successful DevOps toolchain, you’ll want to incorporate tools that surface incidents and changes on your core DevOps platform and shared repositories. The more centralized you can keep all reports on incidents and changes, the better. The goal is to create a single source of truth that makes it easier to identify and fix issues.

• Continuous feedback: A core tenant of DevOps, continuous feedback is a practice that focuses on tracking user behavior and customer feedback about your core products and building actionable data to inform future investments in new features and system updates. This can include NPS survey data about how users are navigating your product. It can also include tracking and modeling user behavior in the product itself.
  
  To build a continuous feedback practice, you’ll want to identify core areas in your product and even outside it in places like social media and reviews where you can identify unexpected user behaviors and customer pain points. Look for tools that enable you to model and analyze user behaviors. You also might consider social listening tools, which you can use to track historical patterns on social media and review sites.

Security and DevSecOps tools

As DevOps has evolved as practice, it has underscored the need to move past more traditional approaches to security which was often siloed from the core SDLC. To ensure you’re shipping high-quality code, making security a core part of the DevOps practice is important. This practice is commonly called DevSecOps, which seeks to integrate security into every stage of the SDLC and make it a core part of CI/CD pipelines.

Companies that invest in DevOps often find the need to invest in also building a DevSecOps practice to ensure software security. This typically involves several tools that help organizations model potential threats and apply automated security testing at key stages of the SDLC. While organizations often try to grab individual tools to create a solution, integrated products like GitHub Advanced Security, can reduce the friction of bringing DevSecOps to your teams. By complementing their DevOps toolchain with DevSecOps tools, companies will often look for the following solutions:

• Threat modeling: Here’s a truism: It’s a lot easier to find security vulnerabilities and potential weak points when you’re developing software instead of after you’ve released it. Threat modeling is a practice that DevSecOps practitioners will engage in from the early planning stages of the SDLC to anticipate any issues and develop plans to solve them.
  
  DevSecOps organizations today will also invest in threat modeling tools that leverage automation and monitoring to proactively identify threats and mitigation efforts. The best tools survey application and infrastructure threats, and will automatically track changes in the underlying codebase and infrastructure architecture.
  
  Look for solutions that can integrate with your core DevOps toolchain to provide updates to relevant people on your team and show risk evaluation scores throughout the SDLC.

• Security dashboards: Having a single view of your security profile including potential risks, testing coverage, alerts, and more is critical for any DevSecOps practice. Security dashboards are often used to collate and break down all relevant security information and provide a quick way to triage issues and assign tasks. At GitHub, we include a security overview page with GitHub Advanced Security to help showcase risk categories across projects and repositories and alert details, too. Ideally, you should look for tools that integrate with your wider DevSecOps security toolchain and offer a single view of your security profile.

• Static application security testing (SAST): SAST tools are used to evaluate code before it is run to identify any potential security risks or vulnerabilities. Importantly, these tools do not need a running system to execute but can be performed on a static codebase.
  
  The best tools will integrate directly into a shared repository and search out any security vulnerabilities, conduct dependency reviews, scan for any confidential password or secrets, and identify coding errors before they make it into production. These tools will also make it simple to find, triage, and prioritize fixes for any problems in your codebase.
  
  You’ll ideally want to look for a solution that integrates with your repository and can be automated to build out issues based on analysis. At GitHub, for instance, we have a SAST tool called Dependabot that analyzes all dependencies for any known security vulnerabilities—and it’s directly integrated into every repository on the platform.

• Dynamic application security testing (DAST): DAST is used to imitate malicious attacks on an application to find any potential vulnerabilities that might risk its real-world security. DAST tools typically analyze applications in pre-production environments to help DevSecOps practitioners identify any possible security flaws before they make it into production. These flaws typically include underlying issues attackers can exploit to run SQL injection attacks and cross-site scripting (XSS) attacks, among other things.
  
  The best DAST tools will integrate with your CI/CD platform of choice so you can automate their deployment within the wider SDLC.

• Interactive application security testing (IAST): IAST solutions are used to identify and profile risks and vulnerabilities in running applications—most often earlier in the SDLC before a release is made. These solutions leverage software instrumentation to monitor and collect information in pre-production environments through manual and automated tests. The best IAST solutions will include software composition analysis (SCA) tools to identify any open source component vulnerabilities.

• Container image scanning: Due to their lightweight architectures, containers have made it simpler for DevOps organizations to build, test, deploy, and update applications in a fast and flexible manner. But large-scale container environments also introduce security risks due to the number of surface areas and potential for vulnerabilities. To mitigate against any risks, DevSecOps practitioners will leverage container scanning tools to identify issues in the container registry, scan container clusters at runtime, and prevent vulnerabilities from making it into production. Look for tools that can be integrated into your CI/CD pipeline and automated to run at specific points in your SDLC before a deployment—including the build, integration, and packaging stages.

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