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Tag: continuous delivery

Tips for Increasing Developer Velocity in the AI Age

These days, everyone is trying to improve release frequencies. Under mounting pressure to deliver more, many software teams are considering new strategies to enhance their developer velocity, like AI-assisted code development and platform engineering. AI has been a boon for automatic code generation, but all the code in the world doesn’t matter if you can’t deploy it into production efficiently.

Another point is that velocity isn’t always measured productively, said Dori Exterman, chief evangelist of Incredibuild and a self-proclaimed ‘productivity freak.’ Too often, managers place value on quantitative elements, such as points per sprint and unrealistic metrics, when they really should be considering the quality of these components and how easy they are to maintain in the long run.

I recently chatted with Exterman and other experts about tactics organizations can use to enhance their development velocity. Below, we’ll consider some helpful methods, from optimizing testing automation to creating internal development platforms (IDPs) and evolving your CI/CD processes. Essentially, optimizing the plumbing will be necessary to match the productivity of instant code generation—otherwise, the vision of continual release will remain stunted.

New AI, Same Plumbing…

No tech article today can mention developer productivity enhancements without a nod to AI. Just take the interest in Copilot, for example. Among GitHub Copilot users, 40% of the code they’re checking in is now AI-generated and unmodified, according to a Microsoft executive. And GitHub reported that 88% of Copilot users feel more productive when using the tool.

In contrast to past hype cycles, the latest AI wave is not just a passing fad—it will have long-lasting effects, said Exterman. However, most of our current DevOps processes were constructed for manual code writing, not for the era of AI-assisted development.

If you quadruple the amount of water you use but use the same plumbing system, you can’t push more water through, said Exterman. Similarly, if you amplify the amount of code you’re producing but don’t evolve your build processes, staging and testing, then DevOps suddenly becomes the new blocker.

Ways to Accelerate Developer Velocity

So, how can we improve development efficiency and productivity in the age of AI? Here are some actionable strategies for overcoming these bottlenecks and improving developer velocity.

Accelerating the Plumbing

Exterman stresses the importance of leveraging AI to enhance the foundational processes of development. This involves using techniques such as distributed computing and smart test selection to streamline build and testing processes. By optimizing these foundational steps, development teams can ensure that their coding efforts translate into efficient and rapid progress.

Optimizing Testing through AI

AI’s potential not only lies in code generation—it extends to optimizing testing processes. Exterman suggests tools like Launchable that allow AI to prioritize tests that matter most. This strategic approach to testing enables teams to focus on high-impact areas while maintaining comprehensive test coverage.

Embracing Test Automation

Exterman underscores the importance of comprehensive test automation. Rapid iteration loops are essential for maintaining velocity, and automation facilitates fast feedback cycles. This approach ensures that each code change undergoes rigorous quality checks, preventing errors from propagating downstream.

Keeping an Eye on Technical Debt

One of the potential downsides of increasing programming velocity is the accumulation of more technical debt. As more development occurs, the temptation to compromise on documentation, unit testing and best practices grows. Exterman emphasizes that velocity must be pursued in tandem with managing technical debt to ensure the long-term health of projects.

Investing in Platform Engineering

Recently, we’ve witnessed a paradigm shift toward platform engineering. Just over half of IT organizations have already embraced platform engineering, according to the 2023 State of DevOps Report. Exterman emphasizes the need for a robust internal developer platform that supports the seamless transition of code from development to staging and production. For instance, tools like Terraform and Earthly play a pivotal role in establishing testable environments and reproducible builds.

“Bring more data services closer to the developers,” said Venkat Ramakrishnan, VP, product management and engineering, Portworx by Pure Storage. “Choose platforms that provide easy-to-deploy services that developers can consume and provide visibility and control into the underlying costs.”

Enabling Observability

Observability, according to Exterman, is also important for gauging efficiency. The ability to continuously monitor and log processes enhances the overall development cycle. One report from Splunk even found that 64% report their investment into observability has exceeded their expectations. Tools from New Relic and Datadog, for example, offer insights that tie quality, delivery and productivity together, enabling teams to make informed decisions.

Evolving CI/CD With Continuous Merge

One method to progressing CI/CD is continuous merge, a new concept that can be implemented with tools like gitStream that aims to decrease the window between a pull request, review and merge.

“CI/CD has done wonders to improve the quality and efficiency of the software development toolchain, but it mainly focuses on things that happen after code has been written and reviewed,” said Ori Keren, CEO and co-founder of LinearB. “Continuous merge is a mindset that extends CI/CD practices further upstream to improve the quality and efficiency of the code review process. We’ve reached a point where code review times frequently cause the most significant delays in the software delivery process; continuous merge is how we fix that.”

Using Cloud-Agnostic Platforms

Cloud-native development has become more popular as organizations seek to design software architectures bred for the natural scalability of the cloud. Simultaneously, both technology and business motivators are resulting in a multi-cloud condition. In this scenario, IT really needs agnostic, open platforms to retain flexibility and reduce bottlenecks.

“Enable developers to build their apps in a cloud-agnostic way so they can scale their apps globally,” said Ramakrishnan. “Much of developer and app momentum is lost when scaling apps. Helping the application teams architect it right by providing cloud-agnostic application platforms like Kubernetes enables developers to move faster, scale faster and reach their customers everywhere.”

Measuring Developer Velocity

The above tips should help improve velocity, but how exactly should you measure success? Well, measuring developer velocity goes beyond simply tracking the number of points completed in a sprint, reiterated Exterman. He emphasizes the importance of focusing on the ability to deliver high-quality products to the market swiftly. He underscores the need for a fast feedback loop for addressing bugs and iterating quickly. AI plays a role here, too, aiding in test writing and enabling continuous delivery.

End Benefits of Improved Developer Velocity

Exterman concludes by highlighting the real-world benefits of increased developer velocity. Faster bug-fixing and release cycles, particularly crucial in industries like gaming, result in improved user experiences and customer satisfaction. He emphasized the significance of aligning key performance indicators (KPIs) and objectives and key results (OKRs) with the aspects that truly matter, promoting a culture of measured and sustainable progress.

September 11, 2023
Monitor Toolchain Health, Too

A funny thing happened on the way to being agile. We adopted a ton of tools to make us more responsive. They really do help us deliver software quickly and efficiently, and they have improved IT processes along with our specific process improvements.

They also created a toolchain that resembles areas of responsibility of old. That makes perfect sense; we still need to store source code, build applications, deploy somewhere, worry about security, etc. And we need to do all of that faster, which is where automation and the toolchain really shine.

But you need to keep in mind the overall impact of the toolchain upon the development process. Now the organization is dependent upon those tools. We went from slow and steady to fast and with a large series of steps along the toolchain. Things are better, we’re delivering quality software at a faster pace, but in some ways, the process risks becoming more fragile. If a tool in the chain shows signs of trouble, you need to be aware of it and on top of the issue almost immediately. The price of ignoring issues in the process can be catastrophic disruptions in software delivery. Losing a CI tool is akin to shutting down customs at an international airport: The source of the problem is big, and the downstream problems are show-stopping.

So while you are tooling your applications, make certain you are tooling the build chain, also. Know when a build that always takes ten minutes suddenly spikes to triple that. Know when a test suite delivers non-breaking errors at an increased rate. Know when space is low on a shared drive that the build tools use. Tool everything, because the software delivery toolchain is now the core of application development and deployment.

Tooling these systems can also offer a warning of problems in a given application. It is possible that the increase in build time is because there are a lot of previously unused libraries being included … and that those libraries have not been properly vetted by security. It is possible that the disk space on that shared drive has PII test data that needs to be cleansed. There is a lot of good that can come from over-tooling the build chain, and unless you have a bit of tooling that slows the process, there is no downside. It won’t take too long to build a list of what to watch, and modern DevOps tools have enough reporting and APIs that it won’t take long to begin watching those items, either.

That is where the people part comes in. Don’t ignore the tooling once it is in place. Build a process to regularly review results and take action when necessary. No tooling is useful if the results are ignored; sadly, we have a long history of spectacular failures that started with, “We saw indications, but didn’t think it was important at the time …” So act on results, even if that just means flagging them for review in the next reporting cycle—just don’t ignore them.

And keep rocking it. The organization thrives on the software you build, deploy and support. It is a massively complex system that has IT staff at its heart, so keep it thriving and watch for indicators of illness in the core toolchain.

July 12, 2023
Harnessing AI in Continuous Delivery and Deployment

In my recent article Revolutionizing the Nine Pillars of DevOps with AI-Engineered Tools, I explained that the continuous delivery pillar is an approach where code changes are automatically built, tested and prepared for release to production; this may be followed by continuous deployment where an approved release is automatically deployed to the production environment. The practices of the CD pillar aim to make releases and deployments less disruptive and more frequent, increasing speed and efficiency.

In this article I explain how AI can help continuous delivery and deployment processes.

Continuous Delivery Use Cases

Orchestration of Test Environments: Teams need a reliable way to prepare and manage various test environments. This includes setting up and tearing down environments, managing versioning and configuration and handling data and resources. AI tools that can help include Harness, and IBM’s UrbanCode, which can automatically spin up and down environments for testing purposes and handle configuration as code.

Execution of Various Tests: This includes system acceptance tests, regression tests, performance tests, user acceptance tests and security tests. The AI tool that could help here is Parasoft’s SOAtest. Its AI can generate test scenarios, ensure full coverage and automatically adapt to changes in the code base.

Performance Monitoring of Release-Candidate-Artifact: Tools need to assess the performance of release candidates, identify bottlenecks and compare performance against previous versions. Dynatrace is an AI-powered tool that provides automatic and dynamic baselining of application performance, helping teams to recognize when performance is lagging.

Automation of Policies for Artifact Acceptance: Rules and policies need to be defined and enforced regarding when an artifact is ready for release. Tools like Harness use machine learning to analyze past deployments and test results to predict the success of future deployments and automate the approval process.

Continuous Deployment Use Cases

Orchestration of Deployment Environments: Teams need to manage deployment environments, including setup, versioning, configuration and data management. Spinnaker is an open source tool that uses AI to manage multi-cloud deployment strategies.

Execution of Deployment Strategies: This includes executing strategies such as Blue-Green, Canary and Feature Flag gradual rollouts. A tool like LaunchDarkly can utilize AI to manage feature flags, analyze usage data and roll out features gradually.

Testing in Production: Testing the new release in the live production environment is crucial. A tool like Sentry can use AI to monitor application use in real-time and identify and alert teams to issues faster.

Restoring Production to a Prior Version: When a problem is detected with a new release, teams may need to revert to a previous stable version quickly. StackStorm is an event-driven automation platform that uses AI to define workflows, allowing you to quickly roll back to a previous version in case of failure.

Updating Documentation and Configuration Management Databases: It’s crucial to keep records up-to-date. A tool like Guru uses AI to automatically update and keep track of documentation, ensuring that it’s always accurate and up-to-date.

Challenges When Transforming CD to use AI

Here are some challenges organizations might face when transitioning their existing CI/CD pipelines to incorporate continuous delivery, continuous deployment and AI tools, along with possible solutions:

Lack of understanding of AI capabilities and uses: One of the first challenges that organizations might face is the lack of understanding of what AI can and cannot do. Some team members may have unrealistic expectations or fear that AI will replace their jobs. Regular training and workshops can help teams understand how AI can enhance their roles rather than replace them. Clear communication about the goals and benefits of incorporating AI can also help allay fears.

Data privacy and security concerns: Using AI tools often involves handling a large amount of data, some of which may be sensitive or private. There are also concerns about the security of AI tools themselves. Establish a robust data governance policy that includes procedures for handling sensitive data. Also, when selecting AI tools, prioritize those that are known for their robust security measures.

Complexity in integrating AI tools with existing systems: Not all AI tools will seamlessly integrate with existing systems and infrastructure. This could lead to technical debt and instability in the pipeline. Prioritize AI tools that have wide integration capabilities. Engage the tool’s support team or hire consultants if needed to ensure the integration is smooth and stable.

Resistance to change: The transition to a new way of working can be met with resistance, especially if team members are comfortable with the existing systems and processes. Change management strategies can help ease the transition. This can involve regular communication about the benefits of the change, training and providing support during the transition.

Cost of AI tools and training: AI tools can be expensive, and there’s also the cost of training team members to use these tools. Start with a cost-benefit analysis to understand if the transition is worth the investment. Look for AI tools that offer a good balance between cost and features. Consider phased implementation to spread the costs over time.

Dependence on vendor support: Given the complexity of AI, there may be a high dependence on the AI tool vendor for support. This could become a problem if the vendor is unresponsive or ceases to support the tool. Choose vendors who have a good track record of providing strong customer support. Also, consider tools that have a strong community around them, which can provide additional sources of help and support.

Summary

Navigating the landscape of continuous integration, continuous delivery and continuous deployment (CI/CD) pipelines can be complex, but it’s a vital process that’s being revolutionized by the introduction of AI tools. From orchestrating test and deployment environments, managing testing and deployment strategies, to automating delivery tasks and quality checks, AI is playing an increasingly critical role in streamlining and enhancing these processes.

However, as with any significant change, transitioning to AI-enhanced CI/CD pipelines is not without its challenges. This includes understanding AI’s capabilities, data privacy and security concerns, technical integration difficulties, resistance to change, cost considerations and vendor support dependencies. By identifying and addressing these challenges head-on, organizations can effectively leverage AI’s capabilities, bringing increased efficiency, reliability and value to their software delivery pipelines.

With proper planning, continuous learning and strategic use of AI tools, organizations can not only overcome these challenges but also thrive, gaining competitive advantages in their software delivery processes. The future of CI/CD is undeniably entwined with AI, and embracing this evolution is an exciting journey that holds the promise of transformative benefits.

June 29, 2023
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.

June 21, 2023
Mastering DevOps Automation for Modern Software Delivery

DevOps automation services are a crucial aspect of modern software development that helps organizations to improve their software delivery processes and keep up with the pace of innovation. With DevOps automation, developers can automate repetitive and time-consuming tasks, enabling them to focus on writing code and delivering features faster and with higher quality. By automating processes such as continuous integration, continuous delivery and infrastructure-as-code, organizations can reduce costs, improve collaboration between teams and respond quickly to changing business needs and customer requirements.

In this article, we will discuss the basics of getting started with DevOps automation services, including popular tools and best practices that organizations can use to implement a successful automation strategy.

Agility and Efficiency for Smart Digitalization

Emerging technologies like multi-cloud, IaC, AI/ML and IoT have only increased the importance of DevOps automation, as they bring new challenges demanding agility, flexibility and efficiency in software delivery processes. Multi-cloud and hybrid cloud environments, for example, can benefit from automating the deployment and management of software across multiple cloud environments. Similarly, AI/ML and IoT require organizations to collect and process large amounts of data in real-time, which can be challenging without automation. DevOps automation can help organizations automate data pipelines and analytics, enabling them to process data faster and more accurately.

Here are some benefits of DevOps automation:

● Speed and Efficiency: DevOps automation enables organizations to deliver software faster and with higher quality. By automating repetitive and time-consuming tasks, developers can focus on writing code and delivering features.
● Agility and Flexibility: DevOps automation provides organizations with the agility and flexibility to adapt to changing business needs and customer requirements. Automated processes make it easier to release software updates and respond quickly to market demands.
● Cost-Effectiveness: DevOps automation reduces the costs associated with software development by reducing manual labor and minimizing errors. It also enables organizations to use cloud resources more efficiently, which can result in cost savings.
● Improved Collaboration: Bringing the different stakeholders along with devs and operations, DevOps fosters a collaborative approach toward enhanced CI/CD. By automating processes, teams can work together more effectively and share knowledge and feedback in real-time.
● Scalability: DevOps automation enables organizations to scale their software delivery processes quickly and efficiently. Automated processes make it easier to deploy software across multiple environments and platforms, which is essential for organizations that need to scale rapidly.

Getting Started With Efficient DevOps Automation

Implementing DevOps automation requires a well-planned strategy, best practices and the right set of tools. In this section, we will discuss how to successfully implement DevOps automation in your organization.

Understanding DevOps
Before you begin with automation, it is essential to understand the principles and practices of DevOps. DevOps is a culture that emphasizes collaboration and communication between software development and IT operations teams to build, test and release software more quickly and reliably. There are some common tools that DevOps teams use for their work, collaboration and communication:
● GitHub: The Git repository hosting service used for version control and source code management functionality, especially for distributed projects.
● JIRA: A project management tool that helps teams plan, track and manage software development projects.
● Slack: A communication platform that enables real-time messaging, file sharing and collaboration.

Choose the Right Tools
Automation is at the heart of DevOps, and selecting the right tools is crucial. Choose tools that fit your requirements, budget and infrastructure. Some popular DevOps automation tools include:

● Jenkins: An open-source automation server that enables continuous integration and continuous delivery (CI/CD) pipelines.
● GitLab: A web-based Git repository manager that provides CI/CD capabilities, project management, and collaboration tools.
● Puppet: An open-source configuration management tool that automates the deployment and management of software across multiple environments.
● Chef: A configuration management tool that automates the deployment and management of software across multiple environments.
● Ansible: Open source tools for automating software provisioning, deployment and configuration management

Identifying Processes for Automation

First, list all the critical processes within your organization that can be enhanced with automation. Start with the repetitive and time-consuming tasks that can be automated, such as code testing, deployment and monitoring. There are a number of popular tools that can help with such automation:

● Selenium: An open source testing framework that enables automated testing of web applications.
● Docker: A containerization platform that enables the creation, deployment and management of containerized applications.
● Kubernetes: The open source container orchestration tool is essential in the DevOps pipeline for orchestration and automation of the deployment, scaling and management of containerized applications.

Creating a Plan

Once you have identified targets for automation and selected tools, create a plan for automation that includes a roadmap and milestones. Start with small projects and gradually expand the automation to cover all aspects of the software development life cycle. Some popular tools for strategizing these life cycles are:

● Trello: A project management tool that enables teams to organize and prioritize tasks using Kanban-style boards.
● Asana: A project management tool that enables teams to track and manage tasks using to-do lists, calendars and timelines.

Training The Team

Automation requires new skills, so it is essential to train your team to use the tools and processes effectively. Provide training and resources to help your team learn new skills and techniques. Some popular education and training resources include:

● Udemy: An online learning platform that provides courses on DevOps automation tools and techniques.
● Coursera: An online learning platform that provides courses on DevOps and related topics.
● Pluralsight: An online learning platform that provides courses on DevOps automation tools and techniques.

Implementing Continuous Improvement

A continuous feedback loop is essential for DevOps’ iterative process and continuous improvement. Integrate mechanisms for evaluating your automation processes, identify areas for improvement and make necessary changes. Here are two common tools used to do this:

● New Relic: An application performance monitoring tool that provides real-time visibility into the performance of software applications.
● Splunk: An operational intelligence tool that enables organizations to analyze and visualize machine data in real-time.

June 6, 2023
GitLab Adds More AI and Cybersecurity Capabilities to CI/CD Platform

GitLab this week delivered an update to its continuous integration/continuous delivery (CI/CD) platform that adds additional generative artificial intelligence (AI) and cybersecurity capabilities.

The GitLab 16 release included cybersecurity capabilities such as centralized policy management, expanded compliance reports and controls, compliance dashboards and default Level 3 attestation for the supply-chain levels for software artifacts (SLSA) framework.

At the same time, GitLab this week also made available an emergency patch for two modules that have a path traversal flaw, tracked as CVE-2023-2825, that could allow unauthenticated users to read arbitrary files on the server under certain circumstances.

In terms of generative AI, GitLab 16 added a value stream forecasting capability that automatically generates workflow summaries for senior managers. GitLab is also adding a code refactoring capability and the ability to automatically resolve vulnerabilities to an update to GitLab 16. Previously, GitLab made available a ‘suggested reviewers’ features, an ‘explain this code’ and an ‘explain this vulnerability’ capability that are all enabled by large language models (LLMs) developed by Google. GitLab has also partnered with Oracle to provide additional AI capabilities enabled by machine learning algorithms. In 2021, GitLab acquired UnReview, a provider of a tool that uses AI to identify which expert code reviewers to assign to a project based on the quality of their previous efforts and current workloads.

David DeSanto, chief product officer at GitLab, said with 10 AI capabilities GitLab is providing more of these types of capabilities than any other DevOps platform provider. In addition, those capabilities are distributed across the entire software development life cycle rather than being focused solely on making developers more productive, he noted.

That’s critical, because if AI capabilities are only focused on developer productivity, an imbalance is created; DevOps workflows will not be able to absorb the increased amount of code that will soon be moving simultaneously through pipelines, DeSanto added.

AI will also further fuel adoption of platform engineering as a means for centralizing DevOps management. It’s becoming more apparent that infusing these types of capabilities into DevOps platforms built and maintained by an internal IT team is going to be a major challenge, he added.

There is no doubt that generative AI capabilities will soon become widely integrated across DevOps workflows. That will enable developers to build more secure code faster while at the same time making it simpler for software engineers to maintain large codebases. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated. In fact, AI may ultimately make DevOps, as a methodology for managing IT, more accessible to a much wider range of organizations.

As AI advances, there should still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has previously been as more low-level tasks are automated. The challenge is determining how and where to refocus the efforts of the DevOps teams to add higher value.

May 26, 2023
CDF Report Reveals Lack of DevOps Maturity

A global survey of nearly 26,000 DevOps professionals published today by the Continuous Delivery Foundation (CDF) at the cdCon + GitOpsCon event found 84% of respondents are involved in some type of DevOps activity. However, the report also found just under half 49% are using a continuous integration (CI) or continuous delivery (CD) platform and only 22% are using CI and CD approaches to automate all building, testing and deployment of code in a production environment.

The report, conducted in collaboration with SlashData, found the number of developers using CI or CD platforms only increased two percentage points year-over-year, with the use of self-hosted CI/CD tools dropping from 32% in the first quarter of 2022 to 23% in the same period this year.

The survey also noted that only 7% of respondents this year described themselves as a DevOps engineer or specialist.

Overall, medium-sized businesses have the highest involvement in DevOps practices (94%), followed by small businesses and large enterprises at just over 90%. More than three-quarters of developers (79%) are now using DevOps practices.

The most common DevOps activity is monitoring software or infrastructure performance (47%), followed by application security testing and using CI to build and test code, tied at 37% each, and approving code deployments into production environments at 36%, the survey found.

The average number of DevOps-related technologies used by developers (4.5) has, meanwhile, remained stable over the last year. The most widely used tools address issue tracking (39%), source code management (38%), test automation management (34%), collaboration and knowledge-sharing tools (32%) and application performance management/observability and agile project management tools tied at 31%.

The survey also noted that the percentage of top performers—defined as those with lead times of less than one day—now stands at 15%. Survey results for top performers over the years continue to fluctuate between 13% and 17% of the survey total. On the plus side, 40% of developers have lead time changes of less than one week, the second-highest ranking for this survey.

More than a third of all DevOps practitioners (34%), however, take longer than a week to restore service. Only 12% of DevOps practitioners take less than an hour to restore service.

In general, the survey finds that organizations that use 10 or more DevOps tools are twice as likely to be high performers.

Fatih Degirmenci, executive director for the CDF, told conference attendees the consortium, which now oversees nine projects, including the open source Jenkins CI/CD platform, continues to make progress as additional projects are added.

However, while it’s clear that many organizations have adopted multiple DevOps tools and platforms, far too many of them still have a long DevOps journey ahead. Each organization will need to decide whether DevOps workflows need to be adopted to fit their needs, even as the pressure to build and deliver software faster continues to mount. In fact, at a time when more organizations than ever are dependent on software to drive mission-critical digital business transformation initiatives, time may be running out to adopt DevOps workflows at a deeper level if organizations hope to stay competitive.

May 8, 2023
The Fallacy of Continuous Integration, Delivery and Testing

We know that continuous integration and continuous delivery (CI/CD) have become a DevOps best practice. And many have learned that by adding continuous testing (CT), they can create a virtuous loop, ensuring perpetual code quality and security. They’re not wrong. Yes, testing continuously is good practice, but some incorrectly equate the concept of continuous with monitoring, thinking they’re able to see everything that could eventually affect a customer’s experience (CX) with their software application. That’s a fallacy. Many of the most glaring blind spots that wreak havoc on CX exist outside the environment that CI/CD/CT is able to “see.” CI/CD/CT, even when supported by application performance monitoring (APM), doesn’t and cannot monitor for and alert to blind spots across the internet stack. And there are many. Only internet performance monitoring (IPM) can take CI/CD/CT as input streams and shift to continuous resilience as a measurable outcome.

How CI/CD/CT Typically Works

As customer requirements evolve, software development teams write code that triggers a new software build. In the continuous model, each new build moves to a runtime environment for integration and quality assurance and, ultimately, is deployed to end users across public or hybrid clouds. This is an application-specific view of the world.

Since quality assurance is part of the typical continuous process, monitoring is an assumed benefit or outcome. This assumption might be because many now complement their CI/CD/CT processes with APM. What is achieved with APM in this approach is testing, not monitoring. While testing provides a better picture of performance at the application level, it doesn’t provide full visibility into the blind spots that exist between the application layer and the end user.

APM Isn’t Enough

While APM tools focus on code—including everything that negatively affects an application such as database wait times and inefficient code—IPM focuses on the internet, which has become your network. IPM monitors what impacts your customers, workforce or application (API) experiences across the internet.

While APM and IPM may seem similar, they are nothing alike. They may share common tools, such as using synthetic agents, but the difference is in what they monitor and how. In this age of highly distributed applications, that difference means everything.

Where APM can “simulate” what a user may see from cloud location to cloud location, which can confirm application performance, this simulation isn’t true or comprehensive monitoring because it is blind to network-related problems with end-user experience, which are vast. Customers and employees each have a unique internet performance fingerprint and access applications from myriad devices across multiple locations and network infrastructures, not through the individual cloud locations that APM synthetics simulate.

IPM approaches synthetic testing more holistically, replicating multiple user journeys across a much more complex Internet infrastructure to proactively flag performance issues before they impact end users. This true monitoring takes place 24/7, not as point-in-time tests.

Forensics vs. Monitoring

With APM and similar observability solutions that embrace real user monitoring (RUM), it’s important to focus on what monitoring really means. APM passively collects data that is a proxy for user experience, enabling postmortem assessment of causes and possible fixes, but after the fact. Observability looks at logs, metrics and traces and provides an assessment of whether users are having a good experience or why they did not. Neither solution by itself will prevent poor experience, and many fixes won’t take place until a postmortem analysis feeds the next iteration. The poor experience has already done its damage, and the costs are already sunk.

With thousands of potential blind spots across the globe that can slow or disrupt applications, from ISPs and wireless carriers to CDNs, it’s not hard to grasp the increasing and differentiated importance of IPM. Where the combination of CI/CD/CT with APM can proactively prevent deployment of poor code that would affect user experience, only IPM is continuously replicating – even more valuable than monitoring–millions of experiences across a dynamic multi-node network where the unexpected is now expected.

Internet Resilience Vs. Retrospection

Businesses today understand the importance of resilience: It’s a board-level concept. Any business that has lost market share and market cap during sustained downtime or poor user experience understands what’s at stake: Millions if not billions of dollars. IPM is squarely focused on “internet resilience,” and its pillars are availability, reachability, performance and reliability. For businesses reliant on the cloud and employers operating with highly distributed workforces, retrospection alone is a risky proposition. Now that the internet is the new corporate network, internet resilience is a new organizational mantra.

The Takeaway

Whether your organization embraces CI/CD/CT already or is rethinking its approach to DevOps, this article should give you pause. Your job–perhaps as part of a larger team–is to catch performance issues and potential disruptions with your application before client impact is realized. Without IPM, only part of that job is being done.

Why is this so important? Even conservative estimates put the cost of an outage at $6,700 per minute (Gartner) and the “cost of slow,” where something causes an application to perform poorly and it goes unnoticed, is likely significantly higher because it happens all the time. The fallacy isn’t that there’s value in CI/CD/CT–there is. The fallacy is that you’re in control of the entire CX of your internet-deployed products without monitoring the internet stack and ensuring internet resilience. Only by adding IPM can you have this assurance.

April 20, 2023
env0 Extends Workflow Platform for Provisioning Infrastructure

env0 today announced it has added support for additional infrastructure-as-code (IaC) tools and the Microsoft Azure DevOps platform to its workflow automation and management platform.

Fresh from raising an additional $35 million in funding, env0 CEO Ohad Maislish said while Terraform remains the most widely used IaC tool, there are now more organizations using alternatives such as CloudFormation from Amazon Web Services (AWS) along with Pulumi and Terragrunt.

Other capabilities added to the env0 platform include automated drift detection, multi-tier workflows, auditing, external logging platform integration, support for plug-ins and expanded role-based access controls and OpenID Connect (OIDC) authentication.

Finally, env0 is extending its existing Terraform support to include support for remote backend, module registry, remote plan, and the ability to plan and apply pull requests. The capabilities are especially critical for organizations that are now building microservices-based applications that require more sophisticated workflows to provision infrastructure resources, noted Maislish.

Most IaC tools are designed to enable developers to address technical implementation and provisioning issues without regard for user experience, security and governance, or business goals. The env0 platform makes it possible to create infrastructure environments with guardrails in place that enable DevOps teams to centrally address governance issues, noted Maislish.

In general, there’s now a lot more focus on continuous delivery (CD) as organizations look to continuously update applications, said Maislish. That requires a more structured approach to provisioning infrastructure across teams of developers that is managed by a platform engineering team, he added. The challenge is that CD is dangerous—in the sense that mistakes can easily be made, noted Maislish.

It’s not clear whether organizations are embracing platform engineering to centralize the management of DevOps workflows, but the number of organizations that have mastered CD best practices remains low. Most organizations are adept at continuous integration (CI), but given the number of unique platforms on which applications are deployed, CD has proven more challenging to adopt. In theory, a platform engineering team should make it easier for DevOps teams to define a consistent approach to CD that could be adopted across an organization.

Regardless of the DevOps approach, the overall goal is to improve developer productivity. The problem is that in the absence of any guardrails, developers have been provisioning cloud infrastructure as best they can. Not surprisingly, cloud computing environments are rife with misconfigurations that create vulnerabilities that cybercriminals have become more adept at exploiting. The fundamental issue is not such much the cloud infrastructure that is available as much as it is the processes employed to provision the resources. In their rush to build and deploy applications, developers will sometimes ignore best practices unless guardrails force the issue.

One way or another, IT organizations are exercising more control over cloud computing environments as the security of software supply chains becomes a bigger issue. The only thing that remains to be resolved now is how to put much-needed guardrails in place in a way that adds the least amount of friction possible.

March 29, 2023
Balance Software Quality, Dev Speed and Cost With CI/CD

In many cases, success in the business world relies on software, be it a compelling mobile app, desktop application suite, interactive website or even an embedded system for the IoT. Because of this need, companies with mature software development processes enjoy a significant competitive advantage over those without. Part of fostering this maturity involves crafting quality software with high velocity but low costs.

Modern software development methodologies, like Agile and its variants, play a key role in crafting applications in an effective fashion. As more companies adopted an Agile approach, the movement also fostered DevOps, a management structure combining software engineering and network administration teams while leveraging automation to build apps more quickly. In many cases, the ultimate goal for these adopters involved achieving the programming Holy Grail of continuous integration and continuous delivery (CI/CD).

So, let’s take a closer look at continuous integration and delivery and how it helps companies successfully balance quality, speed, and cost in their software development projects. In a competitive global economy, adopting this approach remains a crucial differentiator, whether or not a business also fully adopts DevOps. In either case, understanding the benefits of CI/CD helps your software company achieve its business dreams.

A Closer Look at DevOps

As noted earlier, DevOps somewhat grew out of the Agile movement, but it isn’t a software development methodology in and of itself. Instead, it involves a company merging its software development team and its network administration staff largely responsible for helping the process of building applications. It increasingly leverages modern technologies like containers and virtualization for development, QA and production servers and automation for builds and testing processes.

This organizational structure and mixture of tools combine to help companies build high-quality software with high velocity and at a lower cost. A mature community grew around DevOps, including tools, best practices, process experts and more. While DevOps originally served as the sole domain of large enterprises, now businesses of all sizes involved in software development benefit from adopting this approach.

Defining Continuous Integration and Continuous Delivery (CI/CD)

Continuous integration effectively automates the critical software development processes of builds and testing. It especially makes sense for shops using multiple programmers, as it works with the team’s source code version control system to ensure all programming changes are merged and remain in sync whenever a developer checks in their code.

Any check-in typically kicks off a process where unit tests and a build are automatically run to ensure those changes don’t “break the build.” In pre-DevOps days, this tended to be a highly manual effort, leading to inefficiencies in any software development project. In short, the team only discovered any integration issues after a manual test. Automation now plays a crucial role in making this aspect of application building significantly more efficient. CI remains a critical best practice in any DevOps shop.

Continuous delivery shares many similarities with continuous integration, especially its use of automation and the latest software development tools. However, instead of performing builds for internal developers and the QA team, CD focuses more on delivering a software product for release to a user community, customer or the public at large. Again, this is another part of the software development process that led to inefficiencies in the days before DevOps and automation.

In terms of software development for mobile apps, CD ends up being the last part of the development process before the app gets delivered to the App Store or Google Play. It also comes into play with any fixes or feature improvements after the app is on the market. A key aspect of engendering goodwill among your customers directly relates to the speed of app improvements. Adopting a CI/CD approach plays an important role in making app changes with high velocity.

CI/CD is the Heart of Successful Modern Software Development

Simply stated, if your company wants to be a successful software developer, adopting both continuous integration and continuous delivery remains crucial. Following an older, manual approach causes too many bottlenecks and inefficiencies to thrive in a highly competitive industry. No matter how great your startup’s idea or app design is, failure to deliver it with high velocity ultimately results in failure. Once again, this same concept applies to fixes and feature enhancements after the app’s launch.

At the same time, you don’t need to fully embrace the DevOps organizational structure by creating a separate team made up of software engineers and network administrators. At many startups with a small headcount, your developers might handle many of those network tasks, including managing virtual servers and containers. Hiring a network admin or two might be an unneeded luxury.

In the end, if you want to build your startup’s app in the most efficient manner, adopting the CI/CD approach makes perfect sense. It provides speed that allows your team to effectively balance quality, speed and cost, resulting in a great app without overspending. It’s the right approach for any successful software project.

March 10, 2023