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Category: Continuous Delivery

Continuous delivery is a software engineering discipline that allows organizations to consistently develop and release quality software at a rapid pace. It is achieved by automating the software development lifecycle, enabling tasks such as development, testing and release to occur concurrently and continuously.

  • Senser Extends AIOps Reach to Manage SLOs and SLAs

    Senser Extends AIOps Reach to Manage SLOs and SLAs

    Senser is extending the reach of its artificial intelligence for IT operations (AIOps) platform to now include an ability to define and maintain service level agreements (SLAs) and service level objectives (SLOs).

    SLOs are a set of internal performance goals that require access to telemetry data from service level indicators (SLIs), while an SLA is a formal commitment to maintaining specific levels of service.

    Senser CEO Amir Krayden said the company’s AIOps platform collects data from SLIs and then applies predictive AI models to enable IT teams to achieve SLOs and SLAs. The Senser AIOps platform leverages extended Berkeley Packet Filter (eBPF) and graph technology to gain visibility into the entire IT environment versus requiring IT teams to deploy agent software. Machine learning algorithms are then used to aggregate and analyze that data to define thresholds for predicting performance in addition to recommending benchmarks for tracking SLOs and SLAs.

    That approach provides a single source of truth for identifying the actual level of service being provided based on a topology of the infrastructure, network, applications and application programming interfaces (APIs) that makes it possible to identify the root cause of issues and the potential impact of an outage for degradation of performance.

    IT teams have been attempting to achieve and maintain SLAs and SLOs for decades, but given all the dependencies that exist in a distributed computing environment, it’s difficult to achieve that goal. Senser is making a case for applying AI within the context of a platform for automating the management of IT to define and maintain SLOs and SLAs to make it possible to consistently manage SLAs and SLOs in a way that reduces the level of cognitive load that would otherwise be required. Senser is also working toward adding generative AI capabilities to provide summaries that explain what IT events have occurred.

    Collectively, the goal is to provide IT teams with a more efficient holistic approach to monitoring and observability that legacy platforms are not going to be able to achieve and maintain, said Krayden.

    At the core of that capability is eBPF, a technology that allows software to run within a sandbox in the Linux microkernel. That capability enables networking, storage and observability software to scale at much higher levels of throughput because they are no longer running in user space. That’s especially critical for any application that needs to dynamically process massive amounts of data in near-real-time.

    As the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they do need to understand how eBPF ultimately reduces the total cost of running IT at scale.

    Ultimately, the goal is to reduce the current level of complexity that today makes effectively managing highly distributed computing environments all but impossible for IT teams to manually maintain in an era where the pace at which applications are being built and deployed only continues to accelerate.

  • Digibee Adds AI Tool to Accelerate Migration to Integration Platform

    Digibee Adds AI Tool to Accelerate Migration to Integration Platform

    Digibee is leveraging artificial intelligence (AI) to make it simpler to migrate to its integrated platform-as-a-service (iPaaS) environment by converting code into a JavaScript Object Notation (JSON) format.

    A translator tool created by Digibee invokes large language models (LLM) to convert that code into a JSON file that can then run on the Digibee Integration Platform.

    Initially, that capability is being made available to facilitate migrations from the Mulesoft integration platform made available by Salesforce, with support for other legacy integration platforms planned for 2024.

    Digibee CTO Peter Kreslins Junior said this approach reduced the level of time and effort required to switch platforms by using generative AI to map and refactor legacy integration code. That refactoring process doesn’t completely automate the reverse engineering of workflows, but it does substantially reduce the total cost of switching platforms by, for example, automating data migration and simplifying the transfer of configurations, he noted.

    Generative AI also eliminates the need to rely on documentation or maps that either may be limited or non-existent, added Kreslins. Instead, all the integration patterns used can now be automatically discovered by an LLM, he added.

    IT teams will still need to vet and test the code created by the translator tool, but overall, the speed at which any migration effort is made should be accelerated, thanks to the rise of generative AI, in 2024. Many organizations are locked into various platforms today simply because the effort required to rework the code that has been created to extend them has required either too much effort on their part or the contracting of expensive consultants. A migration project that might have taken a year or more can now be accomplished in a few days, said Kreslins. The overall goal is to reduce the current level of pain IT teams experience when migrating between platforms, he added.

    It’s not clear if the rise of generative AI platforms that make converting code into other formats and languages simpler will drive a wave of migrations, but there are many organizations relying on legacy platforms that are not nearly as easy to use as more modern alternatives. As the time and effort required to make those transitions is reduced, more IT teams will be, at the very least, more open to considering their platform options. The providers of those legacy platforms will naturally have to invest in new capabilities much faster to discourage customers from migrating to another platform.

    In the meantime, the amount of code that will need to be integrated is about to exponentially increase as developers rely more on LLMs to generate code. The downstream implications of that increased pace of application development for DevOps teams are profound. It’s not clear to what degree those teams can increase the level of scale at which they today manage application deployments.

    The one thing that is clear is there is no going back because, so far as application development and deployment are concerned, the proverbial AI genie is out of the bottle.

  • From Machine Learning to DevSecOps: Six DevOps Trends for 2024

    From Machine Learning to DevSecOps: Six DevOps Trends for 2024

    The DevOps market is set to grow by 25% annually between 2024 and 2032, fueled by the growing demand for faster software delivery and higher-quality output. Emerging technologies like AI-driven development and serverless architectures have introduced new products in the space, while traditional processes like version control and CI/CD will be further refined. But amongst this innovation, security and quality should remain as organizations’ highest concerns. Looking into the coming year, it’s hard not to see where the DevOps industry is going based on the past year. As such, we have endeavored to provide six distinct DevOps trends we think will be particularly influential in the new year.

    The Integration of AI and Machine Learning

    One of the most notable trends in the future of DevOps is the increasing incorporation of artificial intelligence (AI) and machine learning (ML) into the software development life cycle. The explosion of AI technology in 2023 has set the stage for more predictive analytics in code development, automated testing and intelligent monitoring in the operations phase.

    As Google’s 2023 State of DevOps Report highlighted, DevOps teams feel that AI will contribute most to analyzing data and security, alongside identifying bugs. However, a solid use case has yet to materialize for most teams, and the report shows that early adopters of AI have experienced a minor decrease in their software delivery performance and a significant decrease in their operational performance. So, it’s possible that at this stage teams are implementing AI without considering what problems they’re trying to solve, or without paying full attention to the security and quality implications.

    Emphasis on Increased Productivity

    While AI adoption will be driven by the underlying push for increased productivity in DevOps tools and practices, DevOps teams should focus on drawing maximum value from the existing tools that automate mundane and repetitive tasks, allowing them to focus on more critical aspects of development and operations. This will be made possible by DevOps vendors that provide enhancements to collaborative tools, simplified interfaces and more intuitive workflows, reducing the cognitive load on developers and operations professionals.

    Culturally, the continuing trend towards remote and hybrid work environments is influencing productivity strategies in DevOps, with tools and practices that support remote collaboration and communication becoming vital. Cloud-based DevOps tools are facilitating seamless collaboration, allowing teams to work efficiently irrespective of their physical locations.

    Alignment to Existing Cloud and Microservices Architecture

    We can expect the new alignment of DevOps with cloud and microservices architectures to continue into 2024. This integration is about more than just adopting new technologies — it’s about creating efficiencies that enhance agility and revenue growth.

    The combination of DevOps practices with cloud and microservices enables organizations to leverage the inherent benefits of these platforms. Cloud infrastructure is scalable and flexible, reducing overhead costs associated with physical servers. Microservices allow for rapid development, testing and deployment of independent components without disrupting the whole system. Together with DevOps automation and continuous practices, these architectures accelerate innovation and time-to-market.

    This alignment of DevOps with cloud and microservices will lead to enhanced scalability, resilience, collaboration and customer experiences. By being able to quickly scale, update, and respond to demands, organizations can better satisfy customers and meet market needs, and this, in turn, will drive revenue growth and productivity.

    The Drive for Higher Quality

    While increased productivity and revenue generation will be a key priority in 2024, it’ll be matched by an unwavering commitment to quality. With the rising complexity of software and the increasing dependency of businesses on digital platforms, the margin for error is slimming. Quality assurance practices will become more integrated into the DevOps workflow, ensuring that each release meets the highest standards of performance, security, and user experience.

    Continuous integration/continuous deployment (CI/CD) practices are evolving to include more rigorous and automated testing protocols. These protocols are designed not just to catch bugs but to assess performance under diverse and real-world scenarios. Real-time monitoring tools are being integrated more deeply into DevOps workflows, providing ongoing insights into application performance and user experience. This continuous feedback loop ensures that any issues are identified and addressed promptly, thereby maintaining high standards of quality post-deployment.

    DevSecOps Maturity

    In the same vein, cybersecurity will continue to be a primary concern in 2024, with core DevOps tooling and processes stepping up by incorporating advanced security features. Version control systems will take the lead here by introducing robust access controls, encryption of code repositories and integration with security testing tools to ensure that the code is not only efficient but also secure from vulnerabilities.

    Security integrations in version control will become more advanced, possibly incorporating real-time scanning for security vulnerabilities, tighter integrations with identity management platforms and pattern analysis to predict and prevent potential security breaches in code.

    And as AI is pioneered throughout DevOps tooling, organizations will need to consider the security implications. The Open Worldwide Application Security Project (OWASP) has predicted that data leaks, permission issues and insecure plugins could pose serious security vulnerabilities for software. Teams that can harness growth with AI in 2024 while building sufficient security through DevSecOps practices like backup and monitoring will future-proof themselves.

    Speed and Security

    The speed of DevOps growth, innovation and adoption is only set to increase in 2024. We’ll see teams pioneer new technologies to meet business priorities and revenue targets. In doing so, it’s very easy to jump on the AI or serverless bandwagon and assume that sprinkling it on every technical discipline will make for a bright and shiny future. While some key opportunities in the DevOps space will benefit from the acceleration and tighter integration that new technology brings, organizations looking for lasting growth will need to prioritize quality and security practices.

    The key trend we’ll see in 2024 is teams getting smarter and more focused on how they apply new technology to their DevOps trajectory, delivering faster, more accurate releases that free up time to deliver ever greater value to their customers and business. So, the DevOps winners of 2024 will be the organizations that manage to balance the speed of new technology adoption with robust security.

  • Opsera Leverages AI to Simplify DevOps Platform Migrations

    Opsera Leverages AI to Simplify DevOps Platform Migrations

    Opsera is leveraging a generative artificial intelligence (AI) model it built to enable DevOps teams to migrate from one platform to another.

    Gregg Holtzrichter, chief marketing officer for Opsera, said the Hummingbird AI model makes it simpler to migrate source code from, for example, a Bitbucket on-premises continuous integration/continuous development (CI/CD) platform to GitHub.

    Opsera originally developed Hummingbird as an orchestration engine that integrates DevOps workflows spanning multiple tools and platforms. The primary goal is to make it possible for DevOps teams to unify their workflows, said Holtzrichter.

    At the same time, Opsera is now making it possible to leverage a generative AI model to reverse engineer the source code a DevOps team may have developed to extend the capabilities of a specific platform. That new code then makes it possible to significantly reduce the total cost of switching DevOps platforms, said Holtzrichter. As part of such an effort, Opsera is also now providing support for organizations looking to specifically migrate to GitHub Copilot and GitHub Actions.

    It’s not clear how many DevOps teams might be looking to switch platforms, but with the rise of generative AI capabilities for writing code, interest in platforms such as GitHub has increased, noted Holtzrichter. In addition, support for platforms such as the on-premises edition of Bitbucket is being sunsetted by Atlassian as part of an effort to drive more customers toward the cloud edition of the CI/CD platform.

    Regardless of the underlying platforms involved, generative AI promises to reduce the ability of any vendor to lock customers into a specific platform. As DevOps teams extended DevOps platforms over the past decade, many of them have discovered that the integrations they created and maintained made it all but impossible to switch platforms. With the ability to refactor much of that code based on the insights surfaced by the Opsera platform using generative AI, the time and effort required to switch platforms has been substantially reduced, noted Holtzrichter.

    At its core, Opsera makes 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.

    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, for example, the DevOps Research and Assessment (DORA) metrics defined by Google. In addition, many organizations are keen to discover what best practices should be adopted by other application development teams.

    Now that AI is being applied to that analytics capability, it’s become possible to extend the reach of the Ospera platform beyond analytics to include source code migration. Each organization will still need to determine whether such an effort is worth the remaining time and effort still required, but at the very least, most DevOps teams can take comfort in the fact they can now keep their options a lot more open.

  • What Will DevOps Look Like in 2024?

    What Will DevOps Look Like in 2024?

    The last two years have been marked by significant downsizing in technology companies. As large layoffs at companies like Amazon, Meta and Google make major headlines, there is a general shift away from the “spending cash and growing fast” mindset that free capital and strong growth during the pandemic launched in the tech industry. The name of the game is to be more conservative with spending, and that mindset will drive a lot of investments in the next year. While these cost pressures may feel challenging to technology organizations, there are a few key opportunities as well. Many trends are starting, sustaining and ending, so let’s focus on the top six likely to drive changes in the industry in 2024 and beyond.

    1. AI—Specifically Generative AI—Move Right on the Hype Cycle

    While AI is not new, OpenAI and ChatGPT have created an amazingly accessible and powerful tool that is positioned to drive a lot of value. The hype cycle is still in full swing as dozens of new uses of the technology hit the market daily, and if you’re not trying to build a generative AI-enabled product, you’re getting asked when you will start. In 2024, we will likely see LLMs move into the trough of disillusionment as inflated expectations and projects don’t all play out. However, those organizations with generative AI products that truly deliver value to users should expect to see massive growth. Technology organizations will need to make hard decisions about AI products. You will either find traction in the next year and see huge growth or quickly find it doesn’t work and shift focus back to non-AI products.

    2. Developer Experience Gains Momentum

    While DX has been talked about for years, we’re seeing new investments in tools that empower developers. This trend is partially born out of a need to reduce tool and system sprawl for engineering teams, to empower teams and allow them to move faster and to improve efficiency. While you may not build or buy a DX platform, you will likely start a DX team or committee that better represents the folks building your software. This will lead to some great internal changes and key efficiencies getting attention that may have been previously overlooked. The wonderful thing about this trend is that it both brings efficiencies to the organization and makes developers’ lives easier. It’s a win-win!

    3. Increased Pressure to Reduce Costs

    Even for organizations that have avoided layoffs, most are seeing pressure to shift into a leaner existence. This often means critically looking at all sources of spending and taking on new projects to optimize them. Maybe a cloud re-platform can reduce hosting spend by 20%, or maybe you start looking to remove or combine SaaS tools to reduce seats. You may even see hiring freezes for critical teams. Organizations will have to embrace other trends to increase the efficiencies of their teams to meet business demands here. Don’t be surprised to hear “do more with less” become a mantra around the office.

    4. Increased Accountability Through Data

    While data platforms for technology organizations continue to grow, teams are finding that it’s not enough to just measure a few metrics. Organizations will be required increasingly to use the data they have to show they are delivering value. Partially due to increased pressures mentioned above, teams will need to become more predictable, and better at identifying and handling risk at every stage of development. It’s no longer enough to just build a great development engine, teams must ensure that the engine can power them to their destination.

    5. ESG in Technology

    Focus on DEI in technology continues to require significant investment, however, organizations are starting to consider a broader umbrella of social responsibility. This can range from considering the environmental impacts or carbon production of your servers to changing your software supply chain to avoid supporting particular organizations. These types of requirements are appearing sooner and more overtly in the software development process, and may significantly impact how teams choose to build or implement software.

    6. DevSecOps Continues to Grow

    There has been a renewed focus on security in recent years after some critical attacks exposed vulnerabilities in the software supply chain. It’s not surprising that security is now written into the fabric of how we build and deliver software. Teams are now expected to consider security at all stages of development from product planning to operation in production, and require additional experts, training and resources to both monitor and ensure compliance. Requirements for SaaS products continue to evolve as the last remnants of on-premises solutions evaporate, and teams should be prepared to move quickly as new threats are discovered and need to be patched.

    As the technology industry prepares for 2024, it’s clear that innovation and adaptation will be the keys to success. From the continued evolution of AI and the growing focus on the developer experience to the imperative to reduce costs, increase accountability through data, embrace ESG practices  and fortify security through DevSecOps, technology organizations face an exciting future.

  • Sonar Adds Secrets Detection to Code Analysis Portfolio

    Sonar Adds Secrets Detection to Code Analysis Portfolio

    Sonar has added a secrets detection capability to its portfolio of tools for analyzing code and DevOps workflows.

    Designed to be applied to both finding secrets in code repositories and as applications are developed using an integrated development environment (IDE), this capability is being added to SonarLint, SonarQube and the managed SonarCloud service to identify, for example, passwords, application programming interface (API) keys, encryption keys, tokens, database credentials and other private information.

    Sonar co-CEO Olivier Gaudin said the goal is to eliminate secrets that are accidentally or maliciously stored in source code to improve software supply chain security.

    While it’s important to discover secrets in codebases, Sonar is making a concerted effort to make it simpler for developers to discover secrets before they find their way into a codebase that is one step away from a production environment, noted Gaudin. The SonarLint static analysis tool the company provides can now discover secrets in microseconds using the syntactic and semantic analysis engines the company has now extended, he added. No developer is going to wait five to 10 minutes for a code scan to be completed, said Gaudin.

    A Learn as You Code tool also explains how each secret found in a code segment poses a security risk. The overall goal is not only to remove secrets but, ultimately, reduce the number of instances where secrets are discovered in code, said Gaudin.

    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 that can be used to compromise application security, the need to encrypt secrets becomes a higher priority.

    There is, of course, no shortage of tools for detecting secrets. Sonar is now simply making a case for managing secret detection as part of a larger effort to reduce vulnerabilities in code that cybercriminals can exploit. That approach should help reduce the total cost of securing a software supply chain at a time when forthcoming regulations are going to make organizations that deploy software more accountable for application security.

    In the meantime, thanks to the rise of generative artificial intelligence (AI), the volume of code that needs to be scanned will likely exponentially increase. The issue with general-purpose AI platforms such as ChatGPT is they were trained using code of varying quality that often contains vulnerabilities that are now sometimes incorporated into the code they generate. On the plus side, however, many developers have little to no application security expertise, so it’s just as feasible that ChatGPT will create more secure code than a human developer. Regardless of how the code was created, however, no one will know for sure how secure it is without scanning it first.

    Hopefully, as AI progresses, the quality of the code being generated will only continue to improve. Until then, DevOps teams should continue to assume all code is suspect until proven otherwise.

  • ScienceLogic Unveils Revamped AIOps Platform

    ScienceLogic Unveils Revamped AIOps Platform

    Via an early access program, ScienceLogic this week made available a Hollywood update to its artificial intelligence for IT operations (AIOps) platform that, among other capabilities, provides root cause analysis capabilities that can be invoked via a natural language interface.

    For the first time, this release of the ScienceLogic SL1 platform incorporates predictive and generative AI technologies the company gained with the acquisition of Zebrium in 2022.

    Michael Nappi, chief product officer for ScienceLogic, said Zebrium provided ScienceLogic with an AI model that surfaces insights in a way that is simpler for IT teams to understand and act on. In addition to recommending automated workflows to run, the platform gives IT teams the option to automatically run them when, for example, there is an IT incident that an existing workflow has been defined to address, he added. Each organization will need to determine its level of comfort in automating those processes based on the level of potential risk to the business, noted Nappi.

    In addition, the SL1 user interface displays IT operational information at the business service level to provide IT teams with a guided experience that makes it simpler to prioritize tasks based on their relevance to the business, noted Nappi. There is also now an SL1 toolkit that DevOps teams can use to build or customize PowerPacks templates for monitoring specific processes and services.

    Finally, SL1 is now integrated with Slack and WebEx to streamline collaboration across IT teams. Previously, the platform only supported Microsoft Teams.

    In effect, SL1 now provides IT teams with a cockpit through which they can invoke AI to autonomously manage a wide range of tasks, said Nappi.

    It’s not clear how much advances in AI might one day soon democratize the management of IT, but it is clear the level of expertise required to manage complex IT environments is declining. The overall goal is to reduce dependency on IT professionals, such as software engineers, who are hard to find and retain.

    The rate of change being made to complex IT environments is now also occurring faster than IT teams can track without the aid of AI, noted Nappi. ScienceLogic, in the longer term, is working toward making it possible to interact with a chatbot in real-time to enable IT teams to meet that challenge, he noted.

    Each IT organization will need to decide for itself how heavily to rely on AI to manage IT functions, but in time, many IT professionals are not going to want to work for organizations that don’t provide some level of AI to reduce the level of toil they regularly encounter today. Rather than having to manually perform a series of monotonous tasks, AI should enable IT professionals to act more like supervisors in an IT environment.

    It may be a while before that aspiration is fully realized, but in the meantime, IT teams would be well-advised to start identifying which tasks will soon be automated using AI because roles and responsibilities will evolve. The challenge and the opportunity now is determining how IT teams can add more value to the business as those transitions occur.

  • The Future of Jenkins in 2024

    The Future of Jenkins in 2024

    As software development practices evolve, the tools that support them must also adapt to stay relevant. Jenkins, the venerable automation server used by many DevOps teams, is at such a crossroads as it faces the changing landscape of cloud-native applications and Kubernetes adoption. 

    This article examines Jenkins’ current utility, particularly its features that have made it a staple in CI/CD practices, and addresses the question of whether it will maintain its relevance in 2024 and beyond.

    How Jenkins is Used by DevOps Teams

    CI and CD

    Jenkins has become an integral part of many DevOps teams due to its CI/CD capabilities. Continuous integration (CI) is a software development practice where developers regularly merge their code changes into a central repository. Continuous delivery (CD) is the practice of delivering the software in short cycles to ensure that it can be reliably released at any time.

    Jenkins supports the CI/CD approach in a number of ways. First, it allows developers to automate code integration. This means that whenever code changes are pushed to the repository, Jenkins can automatically build and test the project. Second, Jenkins allows for the continuous delivery of software by automating simple deployment processes (although it lacks full deployment orchestration capabilities).

    Build Pipeline Visualization

    Another key feature of Jenkins is its build pipeline visualization. This feature allows DevOps teams to visually represent the process of software delivery from code integration to deployment. The pipeline visualization provides a clear and intuitive representation of the software delivery process, making it easier for teams to understand and manage the process.

    Build pipeline visualization in Jenkins is customizable, allowing teams to tailor it to their specific needs. For example, it can be customized to show the status of each stage of the delivery process, from code integration to testing to deployment. This allows teams to quickly identify any issues or bottlenecks in the process and address them.

    Automated Testing

    Automated testing is another area where Jenkins shines. With Jenkins, DevOps teams can automate the process of testing their code by running scripts and integrating with automated testing tools as part of the build process.

    Jenkins supports a wide range of testing frameworks and tools, allowing teams to choose the ones that best fit their needs. Moreover, Jenkins can be configured to automatically run tests whenever changes are made to the code. This ensures that any issues or defects in the code are detected early and can be addressed promptly.

    Workflow Scripting

    Workflow scripting is another key feature of Jenkins in DevOps teams. With Jenkins, teams can script their workflows, automating the process of software delivery. This allows teams to standardize their workflows, ensuring that each delivery process is consistent and predictable.

    Jenkins supports the Groovy scripting language for workflow scripting. This means that teams can use the power and flexibility of Groovy to script their workflows. Moreover, Jenkins provides a user-friendly interface for scripting workflows, making the process easy even for those who are not familiar with Groovy.

    Current Usage and Adoption Statistics

    As of 2023, Jenkins remains a leader in the CI/CD space. With its extensive plugin architecture and large user base, it is used by thousands of organizations worldwide. According to the 2022 DevOps Survey by the Cloud Native Computing Foundation (CNCF), Jenkins was used by over 50% of respondents for automation and CI/CD, maintaining a significant presence in the market.

    According to the Jenkins website, there are over 1 million active Jenkins users and more than 200,000 active installations worldwide, with the platform handling 1 million+ jobs per day. It had about 44% of the global market share for CI/CD in 2023.

    The growth trajectory of Jenkins, however, has shown signs of plateauing when compared to newer, more cloud-native-focused CI/CD tools. This observation is backed by Google Trends data, reflecting a steady but not sharply increasing interest in Jenkins over recent years.

    Despite facing competition from emerging tools designed for cloud-native development, such as GitHub Actions, GitLab CI and CircleCI, Jenkins has responded by evolving its ecosystem. The creation of Jenkins X is a strategic move to cater to Kubernetes users and address some of the criticisms related to cloud-native development. However, as we’ll discuss, Jenkins X has not been readily adopted by the industry as of yet.

    Limitations of Jenkins in Cloud-Native Environments

    While Jenkins is a powerful tool for DevOps teams, it has serious limitations in cloud-native environments. These limitations stem from the fact that Jenkins was originally designed for traditional, monolithic applications, not for the dynamic and distributed nature of cloud-native applications.

    Statefulness

    One of the critical limitations of Jenkins in cloud-native environments is its inherent statefulness. Cloud-native architectures typically favor stateless applications, which are easier to scale and manage in dynamic environments like Kubernetes. 

    Jenkins, however, relies on maintaining state—it stores configuration, build history and user data, making it challenging to scale and manage efficiently in a cloud-native setup. This statefulness requires persistent storage, which complicates deployments in distributed systems where resiliency and horizontal scaling are key. Moreover, the risk of data loss or inconsistency increases in cloud-native environments, particularly during scaling operations or when recovering from failures.

    Configuration Complexity

    Jenkins is often criticized for its complex configuration requirements. Setting up Jenkins in a cloud environment involves intricate configurations that are not only time-consuming but also prone to errors. This complexity is exacerbated when integrating with modern cloud-native tools and services, requiring extensive customization and maintenance of plugins. 

    In addition, Jenkins’ traditional master-slave architecture was not designed for dynamic cloud environments, where resources need to be provisioned and deprovisioned rapidly. This leads to a significant operational overhead for DevOps teams.

    Immutable Infrastructure

    Adopting an immutable infrastructure approach is a cornerstone of cloud-native environments, promoting the idea of replacing or rebuilding servers rather than modifying them. 

    However, Jenkins’ traditional model does not align well with this principle. It typically requires modifications to its runtime environment for updates or changes, contrasting with the cloud-native approach of immutable deployments. This can make it challenging to maintain consistency and reliability in cloud-native systems where automation and minimal manual intervention are paramount.

    Microservices Limitations

    Jenkins was originally designed for monolithic architectures and doesn’t naturally fit into microservice-based applications, which are prevalent in cloud-native environments. In microservices architectures, applications are broken down into smaller, independently deployable services. 

    Jenkins, however, tends to treat applications as monolithic units, making it less efficient for handling multiple, independent microservices. This limitation affects its ability to provide fine-grained control over individual services’ build, test and deployment processes. Consequently, Jenkins may struggle with the high degree of automation, flexibility and scalability required in microservices environments.

    What is Jenkins X?

    Jenkins X is an open source project that provides pipeline automation, built-in GitOps, and preview environments to help teams collaborate and accelerate their software delivery at any scale. It is an improved version of the original project, designed specifically for Kubernetes, a popular container orchestration platform.

    Jenkins X streamlines the technical aspects of building, testing, and deploying software. It automates the installation of popular tools like Helm and Draft, making it easier for developers to get started. It also provides pre-configured pipelines so teams can start delivering software right out of the box.

    Jenkins X also introduces a new Jenkinsfile format, Jenkinsfile.yaml, which provides a more straightforward and human-readable way to describe pipelines. This new format, combined with the automation provided by Jenkins X, helps reduce the complexity of pipeline configuration and management in Kubernetes.

    Limitations and Criticisms

    Jenkins X has not been widely adopted by the software industry and has not achieved the respect and prominence of its predecessor, Jenkins. Let’s review the main limitations and criticisms of the tool.

    Lack of Maturity

    Jenkins X’s relatively recent entry into the market means it has not had the time to mature like its predecessor. This lack of maturity can manifest in several ways, including fewer features compared to established tools, fewer community-tested plugins, and potentially more bugs or unresolved issues. Due to the fast-paced evolution of cloud-native technologies, tools need time to adapt and stabilize; Jenkins X is still in this phase of its lifecycle.

    Moreover, a mature tool typically has extensive documentation and a large community of users who can offer support and share best practices. While Jenkins X has seen growth in its user base, it still lags behind more mature CI/CD solutions in these areas. This can lead to challenges when teams are looking to solve complex problems or when they require advanced support.

    Limited to Git Projects

    Another criticism of Jenkins X is that it’s limited to Git projects. While Git is the most popular version control system, it’s not the only one. Some teams may use other systems like Mercurial or SVN, and these teams would be unable to use Jenkins X.

    Even within the world of Git, Jenkins X has limitations. For example, it doesn’t support all Git hosting platforms. While it works with popular platforms like GitHub and Bitbucket, it doesn’t support others like GitLab. This can be a significant limitation for teams that use unsupported platforms.

    Community Support

    Finally, there are concerns about community support for Jenkins X. While the project is open source and theoretically has the potential for wide community involvement, some critics argue that it’s primary source of support is CloudBees, the company behind Jenkins.

    These critics worry that if CloudBees were to withdraw its support, the project could falter. They also argue that the project’s focus on certain tools and platforms (like Kubernetes and Git) reflects CloudBees’ business interests more than the needs of the broader DevOps community.

    Conclusion

    In conclusion, Jenkins’ role in the CI/CD landscape has been pivotal, and its continued evolution with projects like Jenkins X demonstrates an effort to adapt to the needs of modern, cloud-native environments. While Jenkins X has yet to reach the widespread adoption and maturity of its predecessor, its specialized focus on Kubernetes and GitOps reflects the shifting priorities in software development.

    Jenkins, despite showing signs of plateauing in growth, still possesses a strong user base and an established presence in the market, supported by a vast plugin ecosystem and a robust community. Its limitations in cloud-native contexts—such as managing statefulness, configuration complexity, and supporting immutable infrastructure—pose challenges that Jenkins X seeks to address.

    As we approach 2024, the relevance of Jenkins will likely hinge on its ability to integrate more seamlessly with cloud-native technologies and the willingness of the community to evolve its ecosystem. The breadth of its adoption and the depth of its integration in existing development pipelines mean Jenkins will continue to be a significant player in the DevOps toolchain. However, its long-term relevance will depend on the project’s ability to innovate and remain adaptable in a landscape that is rapidly embracing cloud-native solutions.

  • Copado Expands Beta of Generative AI Tools for Salesforce DevOps Platform

    Copado Expands Beta of Generative AI Tools for Salesforce DevOps Platform

    Copado today announced it has expanded a beta program through which it provides application development teams building applications for the Salesforce software-as-a-service (SaaS) platform access to generative artificial intelligence (AI) capabilities enabled by ChatGPT.

    David Brooks, SVP of Evangelism for Copado, said the company, via the usage of vector databases, has exposed instances of ChatGPT to everything in its knowledge base that has been collected over the last 10 years. That approach enables Copado to extend the large language model (LLM) used to train ChatGPT without exposing any data that might be used to one day train the next version of the generative AI platform.

    The goal is to enable DevOps teams to generate code and tests faster while simultaneously reducing the time required to handle support calls, said Brooks.

    In addition, Copado is using ChatGPT to enable impact analysis and conflict detection to reduce the level of friction encountered when deploying applications in a production environment. That latter capability is critical because, in many instances today, DevOps teams don’t have a firm understanding of how code needs to be deployed, resulting in unnecessary rollbacks of application deployments, noted Brooks.

    Generative AI will also automate the creation of documentation, such as release notes for every application deployed, to reduce the overall level of toil DevOps teams experience, noted Brooks.

    Copado also plans to provide governance tools that will enable DevOps teams to ensure the code written by an LLM is not based on a hallucination that results when trying to reconcile conflicting data, added Brooks. There will, of course, always be a need for a human to review code before it is deployed in a production environment, he added.

    In the longer term, Copado will make it possible for organizations to invoke multiple LLMs to address various functions as they best see fit, said Brooks. In fact, DevOps teams should expect to soon be employing multiple copilots trained to address specific tasks such as writing code, deploying applications, assessing cybersecurity and addressing compliance mandates, he added.
    Ultimately, application development and deployment efforts that today require weeks to complete will soon be accomplished within the same day, said Brooks.

    At the same time, organizations will also be able to better manage application environments that today operate at levels of scale few IT teams can manually track. Generative AI capabilities, in effect, will make it simpler for DevOps teams to effectively manage application environments at much higher levels of scale, said Brooks.

    Each organization will naturally need to determine to what degree they are prepared to absorb that level of innovation, but it’s clear the pace at which applications are built and deployed is about to exponentially increase. The issue organizations will increasingly encounter is that business processes will not be able to absorb the rate at which software can be built and updated.

    It’s not clear just yet to what extent generative AI will transform DevOps, but as advances continue to be made, many of the manual tasks and bottlenecks that today conspire to slow down application development and deployment are about to be eliminated in ways that few would have thought possible a little more than a year ago.