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Category: Continuous Testing

Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate.

  • How TestOps Enhances DevOps and Software Development

    How TestOps Enhances DevOps and Software Development

    Businesses increasingly use DevOps and TestOps practices to develop and deliver high-quality software products. In this article, I will explore the concept of TestOps, its integration with DevOps and the benefits of implementing automated testing to enhance product quality, accelerate release cycles and increase the transparency of CI/CD processes.

    Why DevOps and TestOps Should be Integrated

    TestOps, a combination of testing and operations, is a software development methodology emphasizing the importance of integrating testing throughout the development life cycle. On the other hand, DevOps focuses on bridging the gap between development and operations to achieve faster delivery and higher-quality products.

    The key benefits of seamlessly integrating TestOps and DevOps include the following:

    • Enhanced product quality and accelerated release cycles
    • Increased transparency and efficiency in CI/CD processes
    • Faster bug detection (shifting left)
    • Improved user experience and reduced amount of bugs in the final product

    By properly setting up TestOps, companies can make their DevOps processes more efficient, transparent and bug-free. Well-integrated testing in the CI/CD process allows for faster bug detection, ultimately saving companies resources, money and time.

    Detailed Benefits of Integrating TestOps With DevOps

    Organizations can reap several benefits by seamlessly integrating testing into the deployment pipeline. In this section, I will outline the key goals of integrating TestOps with DevOps and how they contribute to the success of software development projects.

    Integration of DevOps and TestOps for Seamless Shift Left

    Integrating DevOps and TestOps aims to facilitate shift-left testing, enabling organizations to identify and address issues early in development. By incorporating testing from the outset, organizations can achieve the following goals:

    • Early bug detection and prevention through continuous testing: By conducting continuous testing throughout the development cycle, potential bugs and issues can be detected and addressed early on, reducing the likelihood of significant problems arising later.
    • Ensuring clean and efficient code deployment: Integration of TestOps with DevOps enables organizations to establish streamlined processes for code deployment, ensuring that the code is clean, efficient and free from defects, thereby improving the overall quality of the product. By such integration, tests will suit each phase in the deployment process, meaning that the only relevant parts will run when needed and take only required resources.

    Empowering Bug-Free Software Development With TestOps

    TestOps, when integrated with DevOps, empowers organizations to develop bug-free software and enhance product quality by offering distinct bug detection, cost savings and resource optimization advantages. The following benefits can be realized through this integration:

    • Rapid bug identification and resolution: TestOps practices enable organizations to quickly detect and address bugs during the testing phase, minimizing the impact on the development timeline and preventing defects from reaching the end user.
    • Significant cost savings and improved customer experience: Proactively identifying and resolving defects during the testing phase with TestOps reduces post-production defects, leading to cost savings associated with bug fixes and an enhanced overall customer experience.
    • Empowering testers and optimizing resource utilization: The integration of TestOps with DevOps ensures efficient testing activities and resource allocation, contributing to the delivery of higher-quality end products that meet or exceed customer expectations.

    How to Get Started With Test Automation

    While the process of integrating TestOps with DevOps can vary depending on the maturity of the company and the project, certain key steps can be taken to ensure success:

    1. Analyzing critical paths: Identify the essential processes and areas that require testing and prioritize them accordingly.
    2. Starting with small chunks: Begin by changing one area at a time and gradually expand the scope of your testing as you gain confidence in the process.
    3. Reviewing existing tests: Review logs and artifacts of existing tests to identify areas that require improvement or additional testing.
    4. Finding the right specialists: Ensure you have the right specialists on your team to help you with the integration process.

    Also, it is essential to consider the following factors when defining a test automation strategy:

    1. Release frequency and the requirement for diverse test types: Determine the frequency of releases and the types of tests required to ensure that your automated testing strategy is tailored to your needs.
    2. Availability and usability of tools: Choose testing tools that are reliable and easy to use and that can be integrated seamlessly with your existing DevOps tools.
    3. Product maturity: Decide whether to incorporate automated testing from the outset or during the transition to CI/CD, depending on the maturity of your product.
    4. CI/CD environments and testing data: Establish the necessary testing infrastructure and provisioning test environments early to ensure your automated testing process runs smoothly.
  • Tabnine Unveils Beta of Generative AI Automated Testing Tool

    Tabnine Unveils Beta of Generative AI Automated Testing Tool

    Tabnine this week made available in beta a tool that enables developers to use natural language to interact with the artificial intelligence (AI) models embedded within its test automation platform.

    Brandon Jung, vice president of ecosystems for Tabnine, said Tabnine Chat extends generative AI capabilities the company developed to make it simpler for developers to invoke its test automation platform from within their integrated development environment (IDE).

    In addition to making it possible to search a codebase more efficiently, that capability can also be used to explain how a piece of code functions, said Jung.

    Developers can also generate new code via a natural language interface using Tabnine Chat in the same way other generative AI tools do, he added.

    In addition to developing its own LLMs, Tabnine previously announced it will be integrating LLMs provided by Google as part of a strategy to mix and match LLMs regardless of where they are hosted.

    Unlike general-purpose LLMs, the ones used by Tabnine were trained using a narrower base of testing code that the company curated using tests made freely available on the internet. No customer data was used to create any of those tests, so developers do not need to worry their code may inadvertently be accessed by anyone else, noted Jung.

    In general, as AI makes test creation simpler, tests will be run more frequently, said Jung. Over time, those AI capabilities and more frequent tests should significantly improve the overall quality of applications running in production environments, he added.

    It’s now only a matter of time before generative AI is applied across the entire software development life cycle (SDLC). Testing, however, may be one area where it will have the earliest impact.

    In the meantime, the debate over the impact generative AI will have on DevOps continues. Some proponents contend most of the software engineering process is about to become automated, while others suggest there will always be a need for a human to be in the loop of any process automated using AI models. Much of the current level of toil associated with DevOps today will be reduced, but the need for software engineers to manage the overall process will remain, they contend.

    The one thing that is certain is that the pace at which applications are built and deployed is about to be greatly accelerated. Prior to rise of generative AI, testing processes were barely keeping pace, so as more code is created with the help of AI, the need for similar advances in testing will become acute.

    There is as much irrational exuberance about AI as there is fear and loathing, but DevOps teams that committed early on to ruthless automation will naturally be at the forefront of adoption. The immediate challenge is assessing what functions will be automated and the impact those changes will have on the way DevOps teams are currently structured. Ultimately, the goal is to eliminate as many of the bottlenecks that conspire to slow application development and deployment while simultaneously improving the quality of the application experience.

  • Applitools Acquires Preflight to Further Apply AI to App Testing

    Applitools Acquires Preflight to Further Apply AI to App Testing

    Applitools this week revealed it has acquired Preflight, which provides a low-code automation platform infused with machine learning algorithms that makes it possible for developers to write their own tests.

    Applitools COO Moshe Milman said Preflight extends the company’s efforts to leverage artificial intelligence (AI) to shift more testing capabilities left toward application developers using its existing test automation platform. The Applitools platform is compatible with frameworks such as Selenium, Playwright and Cypress.

    One of the major challenges organizations encounter as they encourage developers to run more tests is that many of them don’t have the skills required to write them. Applitools’ AI capabilities reduce the cognitive load required to create and run tests, noted Milman.

    The goal isn’t so much to replace dedicated application testing teams but to enable developers to more easily test code as they build it, noted Milman. There will always be a need for dedicated testing teams to evaluate more complex use cases for applications, he added.

    The issue is, thanks to advances in DevOps automation, the pace of application development is outpacing the testing resources available, said Milman. IT teams need to invest in test automation to close what has become a quality gap because not enough code is being thoroughly tested, he noted.

    Test automation also serves to reduce the total cost of creating tests. That enables organizations to conduct more tests without slowing down application development, noted Milman.

    In the longer term, Applitools also plans to leverage large language models (LLMs) to provide generative AI capabilities alongside existing support for machine learning algorithms to further automate testing, noted Milman. Applitools is already making use of computer vision to automate app testing. Preflight adds a set of tools infused with AI to create tests.

    The reasons behind inadequate application testing aren’t entirely clear. Whether it’s because tests are too difficult to create or development teams simply run out of time because of scheduling delays, the impact is the same. Too often, developers convince themselves they will address any issues that might arise with the next update only to become distracted by other priorities. Regardless of the reason, however, there are more poorly-tested applications running in production environments than many DevOps teams would prefer. In fact, much of the technical debt IT organizations need to manage today can be traced back to inadequate testing.

    However, as testing tools become more automated, the overall quality of applications should improve and technical debt may become less of an issue. In the meantime, automated testing promises to improve overall developer productivity as it becomes simpler for developers to create code that has been, at the very least, vetted by algorithms before being added to a codebase. In effect, developers are running out of excuses for not testing applications before delivering them to unsuspecting end users.

  • HashiCorp Acquires BluBracket to Extend Secrets Management Reach

    HashiCorp Acquires BluBracket to Extend Secrets Management Reach

    HashiCorp this week revealed it has acquired BluBracket to add a set of scanning tools for discovering static secrets to its portfolio.

    James Bayer, senior vice president of research and development for the Secure product line at HashiCorp, said the existing Vault platform for managing secrets is designed to enable developers to manage them as they build applications. BluBracket has developed a secrets management tool that makes it possible to discover secrets in applications that have already been deployed in production environments, he said.

    The amount of secrets management-related technical debt many organizations have can be enormous, especially if they’ve been deploying applications for decades. IT teams need a tool that discovers secrets in applications that, in many cases, were deployed by application developers that are no longer with the company, Bayer added.

    The BluBracket tools make it possible to discover those unmanaged secrets even before efforts are put in place to improve application security, he noted.

    HashiCorp will initially integrate BluBracket’s functionality into HashiCorp Vault to expand HashiCorp’s zero-trust capabilities, and add detection and remediation workflows via additional capabilities expected later this year. That’s critical, because for decades, developers have been storing secrets in applications and forgetting to remove them before an application is deployed.

    In general, HashiCorp is moving to transform its Vault platform into an orchestration engine for managing secrets regardless of where those secrets might be stored, said Bayer. That approach will enable developers to build applications and store their secrets on, for example, the Amazon Web Services (AWS) cloud in a way that can be centrally managed via the Vault platform, he added.

    Most organizations today are at varying stages of embracing DevSecOps best practices to improve software supply chain security. It’s not clear how readily secrets management is being embraced as part of those modernization efforts, but as cybercriminals routinely scan for application secrets stored in clear text, the need to encrypt secrets becomes a higher priority.

    The core DevSecOps issue most organizations are wrestling with is how involved cybersecurity teams should be in application development. There’s clearly a concerted effort to push more responsibility for application security further left toward developers. The challenge is, even when those tools are provided, it’s not clear that developers can distinguish between the severity of one vulnerability versus another.

    Conversely, most cybersecurity teams don’t really understand how modern applications are developed. That doesn’t necessary mean cybersecurity professionals need to dive deep into application development workflows, but it does mean they should, at the very least, understand how applications are constructed to make sure the appropriate security guardrails have been put in place.

    Regardless of approach, it’s clear there is a need for some type of centralized security function to better ensure application security. Unfortunately, the cultural divide between application developers and cybersecurity teams remains fairly wide. The DevSecOps journey ahead will be long, as each organization determines how best to bridge that gap when the supply of available cybersecurity expertise remains constrained.

  • Integral Makes Generative AI Code Review Tool Open Source

    Integral Makes Generative AI Code Review Tool Open Source

    Integral this week launched an open source Robin AI project that leverages OpenAI’s generative pre-trained transformer (GPT) platform to review code changes and provide constructive feedback.

    Integral is a data analytics platform used by health care providers. The company originally developed Robin AI using a set of Bash scripts to create a generative artificial intelligence (AI) tool to assist its internal development team.

    Integral CTO John Kuhn said Robin AI is essentially a bot that functions like a coach for developers. It first reviews code and then surfaces optimization suggestions that developers can instantly apply, said Kuhn. It’s up to each developer to decide whether to accept those suggestions, but Integral is already using it to, for example, make sure all the code being written is human readable, he added.

    Robin AI currently works best on JavaScript code repositories, but can be applied to any codebase residing in a Git repository. Integral has also already developed a GitHub Actions script.

    In addition, as an open source project, Robin AI can be integrated with any number of generative AI platforms that might be built using more domain-specific large language models (LLMs), noted Kuhn.

    Most developers are already using AI tools such as GitHub Copilot to write better code. Robin AI is now extending generative AI capabilities to the code review process as part of an effort to improve the quality of the code being written. In effect, it provides a means to surface common mistakes developers make and enables them to fix those before they are discovered by a colleague.

    It’s not clear how much application development will be driven by AI, but Kuhn said he believes it’s only a matter time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero, he added.

    Of course, not everyone in the application development community agrees with that assessment. But one thing that is clear is that generative AI capabilities will soon be pervasive. In fact, the next frontier will arguably involve integrating generative AI platforms with frameworks that make it possible to automatically apply suggestions and recommendations made by generative AI platforms across an entire enterprise IT environment.

    In the meantime, DevOps teams should be evaluating generative AI technologies in terms of the capabilities they can provide today and their future potential. Many of the manual tasks that conspire to make application development and deployment tedious will become increasingly automated. DevOps teams committed to ruthlessly automating as many processes as possible will naturally be at the forefront of adoption. The challenge and the opportunity lies in determining whether AI platforms can be trusted or if a human must always be in the loop to ensure there are no unexpected outcomes.

    After all, while AI can be applied to automate deployments, it’s not quite as clear whether they can be rolled back if a mistake is made.

  • System Initiative Employs Modeling to Reinvent DevOps

    System Initiative Employs Modeling to Reinvent DevOps

    System Initiative (SI) today made available in private beta a platform that creates simulations of IT environments. The platform leverages a built-in framework to generate the code needed to update an application environment.

    Fresh from raising $18 million in funding, SI CEO Adam Jacob said the ultimate goal is to provide a level of abstraction for DevOps environment that eliminates much of the complexity that creates bottlenecks in DevOps workflows.

    For all intents and purposes, DevOps is fundamentally broken; there are too many manual processes that need to be performed, he added. The SI platform represents an ambitious effort to reimagine how DevOps processes are constructed and maintained, said Jacob.

    At the core of that effort are models of IT environments created by the SI platform that are accessed via visual interface. The automation framework built into the platform then uses the relationships between models to infer configuration dynamically and then automatically generate TypeScript code that a DevOps team can apply to automate a task.

    Feedback loops are also provided in real time to provide insight into the viability of a configuration and eliminate the need for no state files to track and no ‘plan’ versus ‘apply’ stages.

    As more organizations realize how dependent they are on software to drive digital processes, many of them are looking to improve the productivity of developers and the DevOps teams that support them. After a wave of initial progress, many DevOps teams find themselves supporting DevOps workflows using custom code that varies widely in quality. SI is making a case for a digital twin platform that generates a set of code that can be applied more consistently, rather than relying on a DevOps engineer to write code.

    Ultimately, the goal is to launch a new DevOps era to eliminate most of the silos that slow down application development and deployment, noted Jacob. In fact, one of the reasons few organizations today fully automated DevOps across the application development and deployment process is that it’s simply too difficult to achieve using legacy tools and DevOps platforms, he added.

    A recent survey conducted by the Continuous Delivery Foundation (CDF), for example, found only 22% of respondents are using CI and CD approaches to automate all building, testing and deployment of code in a production environment. Instead, many organizations are using a combination of DevOps and waterfall processes to manage application development and deployment.

    It’s not clear how many organizations are willing to fundamentally change the way they have implemented DevOps. But at a time when more organizations are moving to centralize the management of DevOps, many of them are evaluating their options, at the very least. The issue, of course, is that multiple DevOps teams within the same organization have committed to disparate platforms. Enticing them to centralize on a common platform will require not just a compelling technical advantage but also a significant amount of political capital.

  • Cycloid Adds Templates to Simplify GitOps Workflows

    Cycloid Adds Templates to Simplify GitOps Workflows

    Cycloid today added a Bootstrap Stacks capability that provides DevOps teams with the ability to build Git templates or use ones created by the company to define cloud computing environments.

    The capability is accessed via a portal added to the Cycloid platform for automating software delivery. It also makes it simpler for DevOps teams to reuse templates that are compatible with Terraform or Kubernetes environments as needed.

    In the months ahead, Cycloid plans to release Headers, a metadata tool that automatically populates project parameters in display pages and a project management tool to provide a unified view for each development environment, including continuous integration/continuous delivery (CI/CD) pipelines, documentation, automation, logs, cloud costs and carbon emissions.

    Cycloid founder Benjamin Brial said the templates provide a layer of abstraction that makes it simpler to implement GitOps workflows. As a best practice, GitOps enables DevOps teams to unify the management of code to provision IT infrastructure and software delivery. However, providing access to a Git repository alone is insufficient, said Brial. DevOps teams need access to a set of templates that automate the process and eliminate what is otherwise a complex set of tasks, he noted.

    Easily discovered templates also eliminate organizations’ tendency to create redundant automations simply because a development team was unable to find an existing framework that another team already created, added Brial.

    That approach also enables DevOps teams to more easily embrace platform engineering principles to centralize the management of DevOps using a Bootstrap Service catalog embedded within the Cycloid platform, said Brial. That capability makes it possible to enforce GitOps policies in a way the developers will have to respect because the automation frameworks are readily available, he added.

    It’s not clear whether organizations are embracing platform engineering. Historically, adoption of DevOps workflows in most organizations has tended to flow from the bottom up. As a result, it’s fairly common for multiple application development teams within an organization to have adopted multiple DevOps platforms to manage workflows. Advocates for platform engineering are making a case for eliminating many of those redundant platforms in favor of a corporate standard.

    At the core of any approach is the assumption that centralization will reduce costs by eliminating redundancy. Issues can arise, however, when centralization results in loss of flexibility. Many of the teams that adopt DevOps want to be able to add and replace tools as they see fit. If there is a corporate standard that narrows their tool options, it’s just a matter of time before those teams start to look for ways around the corporate standard.

    Each organization will need to strike a balance between flexibility and the need to bring some order to DevOps workflows. In the meantime, however, the pace at which multiple software development initiatives are being launched is likely to force a platform engineering conversation that, in many cases, many organizations have postponed longer than anyone cares to admit.

  • Harness Brings Generative AI Capabilities to DevOps Workflows

    Harness Brings Generative AI Capabilities to DevOps Workflows

    Harness today added a raft of generative artificial intelligence (AI) capabilities to its continuous integration/continuous delivery (CI/CD) platform as part of a larger effort to reduce the level of manual toil that today conspires to create DevOps bottlenecks.

    The added generative AI capabilities include an AI Development Assistant (AIDA), which analyzes log files and correlates error messages with known issues. It also includes the ability to use natural language to define policies for governing cloud assets and costs and an ability to automatically identify security vulnerabilities and generate code fixes.

    Other generative AI capabilities that will be added to AIDA over the next three to six months include automated code reviews along with tools for creating pipelines and launching chaos engineering experiments.

    Harness CEO Jyoti Bansal said these are the first of a series of generative AI capabilities that will be applied across the entire software development life cycle (SDLC) using large language models (LLMs) that the company has trained using publicly available sources. AIDA, at its core, provides a tool for creating custom prompts for launching queries against LLMs and a vector database that Harness has incorporated into its platform.

    That approach means no customer data was used to create the model and none of the data used will find its way into a generative AI platform that is available to anyone, said Bansal. As a result, organizations don’t need to be concerned they light run afoul of licensing issues because proprietary code was inadvertently included in an LLM, he added.

    Most of the existing generative AI capabilities that have been brought to market thus far are designed primarily to improve developer productivity, noted Bansal. Harness is now bringing the benefit of generative AI to DevOps teams that today rely on too many manual processes to manage the automations they build on behalf of application development teams, he added.

    Harness is making AIDA available at no extra charge at a time when many DevOps teams will soon be evaluating the AI capabilities provided to them. Most DevOps teams are not going to want to manage workflows without being able to rely on AI to reduce toil and eliminate bottlenecks. Overall, Harness is forecasting generative AI will drive a 30% to 50% increase in DevOps productivity. In addition, Harness is forecasting there will be a 50% to 75% reduction in the time spent remediating vulnerabilities.

    Bansal said that as that process occurs, Harness is betting more organizations will move to replace legacy CI/CD platform with more modern alternatives.

    Previously, Harness has invested in machine learning algorithms to automate a range of DevOps tasks. Generative AI, in combination with machine learning, will transform DevOps workflows in ways that will ultimately improve both the rate at which applications can be built and their overall quality.

    It’s not clear whether organizations might be willing to replace CI/CD platforms that they have been customizing for decades, but at the very least, that option is now more widely considered than ever.

  • Choosing the Right Software Deployment Tools

    Choosing the Right Software Deployment Tools

    Imagine this scenario: After investing countless hours in developing your software, it is finally ready for deployment. Now, you must select the appropriate software deployment tools to guarantee a seamless release.

    This article will guide you through the concept of software deployment and discuss various tools that cater to different needs—from dedicated deployment tools to comprehensive CI/CD pipeline solutions and GitOps approaches. By understanding these categories and their benefits, you’ll be well-prepared to make an informed decision on which tool is the best fit for your organization’s needs.

    What is Software Deployment?

    Software deployment plays a crucial role in the software development life cycle. It involves packaging, distributing and installing an application on target systems or environments, aiming to make your software accessible and usable for end-users seamlessly.

    In today’s fast-paced world, companies must be able to deliver applications rapidly and reliably. A well-planned software deployment strategy ensures that new features reach users promptly while minimizing downtime and errors during updates.

    What to Look For in a Software Deployment Tool

    When selecting the ideal software deployment tool, consider the following factors:

    • Automation: The tool should automate complex software deployments, making the deployment process faster and more efficient.
    • Continuous deployment: Continuous deployment tools allow you to deploy software repeatedly, ensuring consistent deployments. Choose a tool that automates release management and integrates automated builds throughout the development process.
    • Scalability: The tool should be capable of handling large-scale deployments and distributing software across multiple servers. Opt for a tool that enables applications to deploy to multiple servers simultaneously.
    • Integration: The tool should integrate with version control and other third-party tools, making the deployment process seamless. Look for a tool that integrates with modern DevOps processes and connects automated builds to configuration management.

    Full CI/CD Tools

    Bamboo

    Bamboo by Atlassian is a robust tool that integrates seamlessly with other Atlassian products, such as Jira and Bitbucket.

    It offers first-class support for multiple languages and platforms while providing built-in deployment projects for smooth releases.

    GitLab CI/CD

    GitLab CI/CD, an integral part of GitLab, simplifies pipeline management using YAML configuration files stored within your project repository.

    This approach enables easy collaboration among team members while ensuring consistent build environments across all stages of development.

    Codefresh

    Codefresh is a cloud-native solution designed specifically for Kubernetes deployments, It provides a modern interface to manage pipelines visually, along with advanced real-time debugging capabilities.

    Dedicated Deployment Tools

    Jenkins

    Jenkins was originally conceived as a continuous integration (CI) tool, but many organizations use it mainly for deployment. It is an open source automation server that provides support for building and deploying software projects, as well as to schedule and run tasks at regular intervals. Learn more about Jenkins in this in-depth blog post.

    Harness

    Harness is an intelligent software delivery platform that simplifies deployments through automation and machine learning. Their continuous delivery-as-a-service (CDaaS) approach helps you deploy faster while minimizing risks and errors.

    AWS CodeDeploy

    If you’re using Amazon Web Services (AWS), AWS CodeDeploy might be a perfect fit for your needs. This completely managed service automates code deployments to any instance or serverless Lambda function in AWS, guaranteeing consistent results every time.

    Spinnaker

    An open source project backed by Netflix and Google, Spinnaker offers powerful multi-cloud continuous delivery capabilities for deploying applications at scale across various cloud providers like AWS, GCP, Azure, Kubernetes clusters, etc.

    GitOps Tools

    Argo CD

    Argo CD is a declarative, Kubernetes-native continuous delivery tool that uses Git repositories as the source of truth for application definitions. It simplifies rollbacks, provides audit trails and enforces consistency across environments.

    Flux CD

    Flux CD, another Kubernetes-focused option, automates deployments by monitoring your code repository for changes and applying them to your cluster accordingly. Bonus: It supports multi-tenancy and integrates with Helm charts.

    Weave GitOps Core

    Lastly, Weave GitOps Core offers an open source foundation for managing Kubernetes applications using the power of GitOps workflows. This versatile tool can be extended to fit various use cases while maintaining simplicity in its core functionality.

    Conclusion

    There are many software deployment tools available that can help automate the software delivery process, reduce errors and improve collaboration between development and operations teams. From open source tools like Jenkins to cloud-native solutions like AWS CodeDeploy, each tool has its own strengths and features that make it suitable for different use cases.

    When selecting a software deployment tool, it is important to consider factors such as compatibility, automation, scalability, security, monitoring, deployment environment compatibility, integration with existing tools and support and documentation. By carefully evaluating these factors and choosing the right tool for your specific needs, you can ensure that your software deployment process is efficient, reliable and successful.

    Image Source: Freepik

  • The Case For an API-First SCM

    The Case For an API-First SCM

    Somewhere around a decade ago, about the time DevOps really started to manifest, we began seeing technology companies—including some of the largest ones today—investing heavily in their APIs.  

    API-first companies managed to rapidly gain widespread adoption because of the benefits of being API-driven. Everything from automation and scalability to flexibility, performance, speed and monetization.  Once DevOps thinking made it increasingly clear that automation would solve problems like human error and increase performance and velocity, creating more machine-to-machine interaction became a critical factor in software delivery—because the problem in the technology world was not innovation, but rather integration.

    Twilio is a prime example. The company became famous for its API-driven approach to customer management and even had a famous Silicon Valley billboard trumpeting their success. They changed the game for customer communication for some of the largest companies in the industry, from Netflix to DoorDash to Coca-Cola, by making communication completely invisible to users. These APIs largely served developers by unifying communication on the backend and providing powerful customer-facing capabilities on the business side. But they’re not the only ones.

    API-first SCM

    API-driven companies managed to disrupt entire markets nobody imagined could be disrupted, such as Stripe for payment processing and Slack for immediate communication (their former head of developer relations, Amir Shevat, said that Slack’s developers were literally measured by their API’s active tokens). Companies that harnessed the power of their APIs saw unprecedented growth over the last decade due to the integration power they unleashed. 

    Today, there really is no self-respecting dev-first technology that doesn’t invest heavily in the robustness of its APIs. There are even entire companies today built around API integrations—from marketplaces like Rapid API to security companies like Akita Software to companies like Kong that focus solely on simplifying API gateways.

    The API-First Dev Tool Revolution

    The same transformation that disrupted various business industries is now beginning to take center stage in the technology and dev tools space. This is also part of the developer experience revolution that is the stimulus for all the nifty dev tools in the first place. As developers become the new decision-makers—masters of their own domain—and are no longer being forced to adopt and use technology someone else purchased for them, the experience of using these tools impacts their choice of whether or not to add these tools to their chain. This means that those creating the tools today need to deeply understand the developer’s workflow and optimize their products for this user persona.

    This very same evolution that typical tooling underwent is now impacting age-old tooling in the developer ecosystem—and developer pain and friction are starting to come to the forefront. This includes the need for better and more robust API capabilities, “as-code” (infrastructure-as-code, testing and security) tooling, low-code/no-code and code variations and much more.  

    It’s interesting to see that, even with the cloud-native and DevOps revolution, our most core developer tool—our source code management—hasn’t really changed much in the last 20 years. Git remains ubiquitous, but its design is optimized for developer workflows from two decades ago. It’s long past time for an API-first SCM, and there are a number of use cases that can benefit from better APIs.

    Evolution and Anatomy of APIs to be Git-Native

    Even with the most exciting innovations we’re seeing from Git SaaS tools like GitHub and GitLab—from Co-Pilot to Codespaces, pipelines and more—they’re only as strong as the technology they are built upon. As the world evolves, this fundamental weakness is proving to be a sort of kryptonite.  

    Despite Git being an old-school, monolithic tool with proprietary protocols, Git hosting providers like GitHub, GitLab and Bitbucket have invested a lot into making their services API-driven to cater to tool integration. True, each has its own API, which drove some players integrating with the service (especially in the early days) to integrate via an SDK rather than an API.

    GitHub itself provides a variety of APIs—from the simpler REST through the more elaborate GraphQL. These APIs provide you with the ability to do pretty much anything, but are somewhat cumbersome for some use cases. To deal with the core Git functionality, you are required to directly manipulate objects in the Git database.

    Code Deployment and GitOps

    As engineering organizations grow, the need for greater control of code and infrastructure resources has become a real pain—and this is where GitOps provided the methods and the tools to support automated policy management at scale. This automated policy and governance is largely enabled, machine to machine, via APIs, and many of its controls are built on code and configuration files that are hosted via Git (hence its name: Git Operations).

    When using this methodology, a configuration change is first committed to Git (as the single source of truth that will apply changes in all the relevant places). Only then does this trigger automatic processes to apply these changes universally. In some cases, these automatic processes have to commit files to Git via API.

    Another area where SCM APIs play a key role is versioning and release management.  DevOps teams need to manage the release of new versions of their applications, including creating release notes, tagging releases and creating release branches. These processes, which sometimes take place multiple times every day, include many steps and require accuracy—a comprehensive suite of efficient APIs is critical for such an operation.

    UX Customization

    Today, there’s a huge diversity of stakeholders in modern engineering processes, particularly in the context of game development, but this is also true in any kind of web-based or UI-based engineering, as well. Today, product and design are an integral part of engineering processes, but Git still remains the domain of developers.

    There are times when you’d like to have a unified interface for your asset management that pulls data from multiple APIs and sources—such as Git for source control and Google Drive for large files. The added benefit is to abstract and decouple the underlying technology from the user experience and interface so that users can be free to migrate and move between their technologies of choice. This is true whether at the SCM or the storage level without having to change the interface that the team is accustomed to.

    APIs that efficiently perform the full set of SCM functionality allow developers to use a single backend for managing code versions while still providing end users with the desired experience, regardless of what that might be.

    Versioning-as-a-Service

    SaaS products have made it significantly easier to manage and maintain our services through simple UIs and dashboards. However, this simplicity also has a tradeoff. Have you ever made a simple configuration or code change, quickly regretted doing so and then attempted to revert it via your SaaS console? In most SaaS apps that aren’t managed as code, this happens all the time—from Okta to Datadog and many others. Without the benefits of “as-code” and the version control that comes with it, reverting changes is nearly impossible.

    The most you can achieve is “undoing” a previous configuration—if you can still catch it with a CTRL+Z—simply because engineering didn’t bother (or did not have the resources) to implement versioning. Imagine if this could come as a service for all of your SaaS applications!

    For software developers, API-based version control will unlock the possibility of easily incorporating rich version control capabilities into their product—in the same way they can currently use other capabilities such as database, file storage, etc. 

    The World is Ready for an API-First SCM

    TL;DR: API-first models have delivered some of the greatest innovations that ‘dinosaur’ industries—from payments to communications—needed to move faster and do so securely. It is almost laughable that the technology industry has inherent limitations due to a core technology that has a legacy design. This should not be a technology limitation we should accept.  

    Our source code should be able to be controlled, manipulated and transformed via API to remove human toil from places it isn’t really needed and free people to focus on higher-order problems.  With the widespread adoption of generative AI and smarter machine overlords, we’ve discovered that there are many formerly manual tasks that we can offload to machines. Free SCM APIs to unlock the next generation of innovation—because soon there will be more machines than humans to manage them. APIs are the key to tapping into this power.