Category: Application Performance Management/Monitoring

  • Tricentis Acquires Waldo to Further Advance Mobile Testing

    Tricentis Acquires Waldo to Further Advance Mobile Testing

    Tricentis has acquired Waldo as part of an effort to expand the scope of the automated testing capabilities it provides for mobile applications.

    Waldo adds a software-as-a-service (SaaS) platform that provides IT teams with a no-code tool for generating mobile application tests. Waldo uses a virtual environment to simulate iOS and Android devices in a way that eliminates the need for physical devices to conduct tests. Instead, DevOps teams record their applications and then use the no-code tools to generate tests directly from within a browser.

    Tricentis CTO Mav Turner said Tricentis opted to acquire Waldo because it makes it simpler for IT teams to generate tests that can be integrated across a DevOps workflow.

    There currently is a lot of debate over how far application testing should be shifted left toward developers. In theory, the more developers test applications themselves the fewer issues there should be once an application is deployed in a production environment. However, there is still going to be a need for dedicated testing teams to conduct tests across multiple stages of the application development process to ensure quality, noted Turner.

    The difference is more of those tests can include edge use cases that previously there would not have been enough time to conduct, he added.

    Earlier this year, Tricentis added the ability to test native iOS and Android applications to its Testim software-as-a-service (SaaS) platform as part of an effort to extend the reach of that platform to applications that run natively on a mobile device.

    Testing mobile applications is more critical than ever because the cost of failure—in terms of impact on the business—is now much higher as more organizations depend on software to drive business processes. The challenge is that many of these app developers don’t have a software engineering mindset, so they need testing platforms that automatically create and run tests on their behalf in a way that can be customized for specific use cases.

    Over time, the quality of applications should steadily improve as many of the low-level mistakes that developers make are eliminated. That should improve everything from application security to the user experience as applications are continuously updated.

    In addition, organizations that embed testing within DevOps workflows are steadily reducing the number of issues that need to be addressed before applications are deployed.

    Hopefully, as more artificial intelligence (AI) is applied to the testing process, the number of reasons tests are not run as frequently as they should be will steadily decline. Today, it is still too difficult for many developers to author tests, so they don’t conduct as many as they should. Of course, many of them also simply run out of time when writing the code that drives the application takes longer than anticipated.

    One way or another, developers will run out of excuses for not running tests as accountability for code quality increases.

  • BigPanda Extends AIOps Platform to Embrace Generative AI

    BigPanda Extends AIOps Platform to Embrace Generative AI

    BigPanda today added generative artificial intelligence (AI) capabilities to analyze incidents surfaced by its AI for IT operations (AIOps) platform.

    Blair Sibille, field CTO for BigPanda, said Generative AI for Automated Incident Analysis leverages generative AI platforms based on the general-purpose large language models (LLMs) created by OpenAI.

    The goal is to make it simpler for IT teams to discern the root cause of an issue by, for example, taking advantage of summaries of alerts enabled by generative AI to make it simpler for IT professionals of any skill level to understand what requires their most immediate attention, he added. Today, insights are lost in the sea of alerts that often inundate IT teams, Sibille noted.

    BigPanda has been at the forefront of applying machine learning algorithms to automate IT operations management. Now, the company is incorporating external general-purpose LLMs to further augment IT teams, said Sibille.

    As IT environments continue to be more complex, it’s becoming essentially impossible for IT teams to manage them at scale without relying more on automation. AI technologies take automation to another level, but they won’t eliminate the need for DevOps teams to manage IT environments, added Sibille.

    BigPanda claimed generative AI capabilities will make it possible to reduce the amount of time required to resolve tickets by seven minutes each, with an accuracy rate of 95% during beta testing. Over time, those capabilities not only reduce mean-time-to-resolution but also reduce the level of toil that often conspires to burn out DevOps teams, said Sibille.

    At the core of the BigPanda AIOps platform is a data lake capable of consuming data generated by multiple applications and systems. That data then provides the foundation on which machine learning algorithms are applied to surface insights.

    It’s not clear how much generative AI will automate DevOps workflows, but in effect, more IT teams will be able to take advantage of having the equivalent of a DevOps engineer that they can query any time an issue arises using natural language, noted Sibille.

    AI doesn’t mean that IT outages will be completely eliminated, but it does mean they will become a lot less stressful, he added.

    Naturally, there’s a lot of speculation concerning the impact AI will have on DevOps. Some contend it’s only a matter of time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero. Others argue that AI will simply augment DevOps teams in a way that makes it simpler to manage IT at scale using either the same number or possibly even fewer engineers.

    The one thing that is clear is that AI will soon be pervasive. DevOps teams should start evaluating today which tasks will be automated with an eye toward planning how DevOps roles will evolve as AI advances accelerate.

  • Roadie Adds Scorecard Tool to Backstage SaaS Platform

    Roadie Adds Scorecard Tool to Backstage SaaS Platform

    Roadie today made generally available a tool that automatically tracks and reports how software development teams are performing against engineering standards defined by a DevOps team. The Tech Insights tool is an addition to Roadie’s implementation of the open source Backstage developer portal and is delivered via a software-as-a-service (SaaS) platform.

    Roadie CEO David Tuite said Tech Insights is a proprietary tool that Roadie developed as an extension to its platform. Tech Insights provides scorecards that track software assets in the Backstage catalog and ensure they are meeting predefined quality and compliance targets.

    Tech Insights comes with built-in, pre-defined data sources; alternatively, DevOps teams can define them using external application programming interfaces (APIs) or files in a repository. A rule known as a Check can then be created to validate whether a project complies with the engineering standards defined by the DevOps team. Each DevOps team can create an unlimited number of scorecards.

    In addition, teams can create prompts and notifications to warn engineers when they are about to make a change that will violate an engineering standard. DevOps teams can also employ gamification techniques to show teams how their performance compares to others.

    While Roadie is now adding its first proprietary tool to the platform, Tuite said the company remains committed to enabling organizations to migrate their instance of Backstage to another environment should they ever deem it necessary, he added.

    Originally developed by Spotify, Backstage makes it possible to create and manage a catalog of blueprints that developers can readily consume rather than requiring them to repeatedly build these capabilities themselves. The goal is to create scaffolds that developers can consistently reuse across multiple application development projects.

    Spotify has since donated Backstage to the Cloud Native Computing Foundation (CNCF). However, Backstage currently requires a significant amount of DevOps expertise to set up and maintain, an issue that Roadie is addressing by making it available as a SaaS platform that can be accessed by any application development team, said Tuite.

    The concept of a self-service portal through which developers can address their own needs may not be new, but as an open source project, Backstage is lowering the cost of improving overall developer productivity. At the same time, many organizations are now embracing platform engineering as a means of centralizing the management of DevOps.

    Ultimately, every minute developers spend on tasks other than writing code is one less minute they are spending writing the business logic that drives an application. Measuring developer productivity is, of course, an inexact science. No one knows for sure when inspiration might strike, but if developers are busy maintaining code that they created to provision infrastructure, it’s less likely they’ll be struck by a flash of brilliant insight that improves and application. The easier it is to spin up a development environment, the more likely that inspiration will one day turn into code that runs in an actual production environment.

  • Key Insights From the Stack Overflow 2023 Developer Survey

    Key Insights From the Stack Overflow 2023 Developer Survey

    Stack Overflow has released its seminal yearly developer survey. The 2023 report, which surveyed over 90,000 developers, provides a comprehensive view of the current software developer experience.

    Below, I’ll highlight a few important findings, namely the significant programming language and tooling preferences, the use of AI in the development workflow and what these trends could mean for the DevOps field. I also gathered insights from Joy Liuzzo, vice president of product marketing at Stack Overflow, to dive deeper into some of the findings.

    Developer Technology Preferences in 2023

    Of note is that more and more developers are learning to code online. Learning to code using online resources increased from 70% in 2022 to 80% in 2023. Although many developers (47%) still have a Bachelor’s degree in computer science or the equivalent, these trends underscore the movement toward alternative knowledge solutions, especially for younger programmers. The topmost online resources include technical documentation, blogs, forums and how-to videos.

    JavaScript is still king—it’s now in its eleventh year as the most common programming language. But it’s worth highlighting that Python has overtaken SQL as the third top language. “SQL was solidly in the top three (JavaScript, HTML/CSS, SQL) since 2015, so it’s a big deal for it to have fallen below Python,” said Liuzzo. “We’ve seen Python’s popularity increase based on the number of questions asked on our public site, so we’ve been expecting some shifts.”

    TypeScript and Bash/Shell have also consistently grown in usage over the last few years. “These two languages are involved with the functions of other popular programming languages, explaining their prevalence among programmers,” Liuzzo said.

    PostgreSQL also overtook MySQL as the most common database type. In terms of web frameworks, Node.js and React.js are the most dominant. Other frameworks, like jQuery and ASP.NET, are showing signs of going out of style, likely because they are older web frameworks.

    Spotlight on AI

    The explosion of new AI innovations, such as large language models (LLMs) and chat-driven generative AI tools, had a major impact on this year’s technology findings. In fact, 83% of respondents have used ChatGPT over the past year. This is followed by Bing AI (20.6%), WolframAlpha (13.36%) and Google Bard AI (9.86%). GitHub Copilot was ranked as the top most-used AI developer tool.

    There was a significant spike in using AI tools amongst those learning to code—these folks often see benefits like speeding up learning, increasing productivity and greater efficiency. “Pair this with how they are currently using AI tools to debug and get help (68%) and learn about a codebase (50%), and we see that the commonality is that AI tools enable learning,” explained Liuzzo.

    That being said, there’s skepticism around AI’s accuracy, and only 13% see improved accuracy in coding as a benefit of using such tools. These pain points may resolve as LLMs evolve, but for now, there still needs to be human judgment to catch bugs and avoid hallucinations.

    “AI has a complexity cliff. Much like Helmsman’s complexity cliff found in project management, after a certain point, the ability for AI to handle all the nuances and interdependencies of a solution drops off,” said Liuzzo. “That’s when humans, their ability to apply judgment and have original thoughts, saves the day.”

    So what are some takeaways for those working in the DevOps field, namely DevOps tooling providers and platform engineers? Well, new tooling preferences and habits will continue to evolve in the DevOps field in new ways. Here are some potential examples:

    • More Markdown-based collaboration. Markdown is the most admired asynchronous tool and has increased in popularity this year. New programmers are adopting a blend of Markdown, GitHub Discussions and Notion for their asynchronous tooling. Flavors of Markdown are already being used for comments, merge requests, issues and more.
    • Continued Docker reliance. Docker remains the top-used tool, followed by npm and Pip. Despite the rise of managed container services, Docker is still a popular way to package and distribute software.
    • Challengers to the big three loom in the distance. Amazon Web Services (AWS) is still the most-used cloud platform at 48.62%. It’s followed by Microsoft Azure (26.03%) and Google Cloud (23.86%). Yet, newer developers want to work more with Hetzner and Vercel.
    • Online learning is paramount to knowledge sharing. Given the steady increase in new programmers and their reliance on online resources rather than traditional schooling, DevOps and open source communities should seek to enhance their online footprint by making knowledge like tooling documentation and tutorials easily consumable.

    Last but not least, generative AI is set to enhance programming in many areas and is already becoming embedded into many environments. Just as AI can be used for code generation and debugging, LLMs will likely assist the DevOps side of the equation, helping automate the CI/CD pipeline, optimize infrastructure, detect anomalies and provide more chat interfaces to initiate these capabilities.

    “AI tools are empowering and enabling learning,” said Liuzzo. “Generative AI will democratize coding and grow the developer community by several folds—and that growing number of developers will be the ones using the tools and also verifying and validating the outputs as they learn—bringing the power of the developer community and the technological power of AI together.”

  • How to Keep Your IT Infrastructure Invisible

    How to Keep Your IT Infrastructure Invisible

    IT infrastructure is critical. IT infrastructure enables businesses to run—from day-to-day operational execution to visionary strategic innovation and everything in between. What’s interesting, though, is that people are only aware of it when there’s a problem: HR can’t access the payroll application, customers are complaining about how slow the transactional system is or this month’s cloud bill was double what you expected it to be.

    No one should notice a well-orchestrated environment, but once application performance slows or an outage occurs, people start to grumble. The same is true for IT infrastructure.

    The Top Four Ways Your IT Infrastructure Can Become Undesirably Visible

    Given the complexity of modern hybrid/multi-cloud environments and the applications they support, it’s no surprise that countless things can go wrong—and when they do, your IT team takes the flack.

    If your infrastructure is humming along nicely, that’s a testament to the IT infrastructure team’s hard work and expertise. Sadly, they don’t get the recognition because it’s all invisible to the rest of the organization. It’s only when *BAM!* something happens that the team is scrutinized.

    While there are many ways this can manifest, it usually falls under one of the following four categories:

    • Availability — This may be the most catastrophic because when systems are unavailable, people can’t do whatever it is they need to do.
    • Performance — A system might be available, but that doesn’t mean it’s performing to SLA or user expectations. The result can range from minor annoyance to lost revenue and customer churn.
    • Cost — Runaway expenditures don’t necessarily affect users, but you do not want to be called to account by the finance team or, worse, executive leadership.
    • Compliance — This is another area that may not have a broad impact on user experience, but the repercussions of non-compliance can be financially and reputationally significant.

    Common Infrastructure Culprits and How to Address Them

    To ensure the necessary availability, performance, cost control and compliance to keep your IT infrastructure under the radar, you must understand what can cause problems in these areas. Here are eight common culprits, along with ways you can address them, so your IT team stays invisible.

    Broken or Misunderstood Dependencies
    Your infrastructure is a complex web of interdependent applications and services. Every time you make a change, it could have a rippling effect on the categories mentioned above. Automated discovery and service mapping capabilities enable you to easily trace dependencies throughout your infrastructure to avoid unintended consequences.

    Cloud Waste
    Chances are very high that you’ve got cloud waste. This means that you could be getting billed for a storage block that’s attached to a stopped instance, for compute instances that are not being used, or for unattached load balancers. Finding idle and abandoned resources can keep your monthly cloud bill under control and on budget.

    Un-Breakdownable Cloud Costs
    Cloud bills are notoriously complex, and while cloud providers do offer tools to help you analyze your cloud spend, those tools often don’t answer all the questions your business might have. In addition to adding clarity to such opaque expenses, you need to be able to parse your cloud spend based on what makes business sense to your organization, not what makes technical sense to your cloud provider.

    Poor Capacity Planning
    Your infrastructure needs to meet your business needs, which are continuously evolving. As changes happen, whether it’s increased utilization of existing resources or modified/added resources to support new requirements, workloads could push current capacity and impact performance and stability. You need to be able to forecast infrastructure demands against business objectives to minimize potential issues before they happen.

    Poor Workload Placement Decisions
    Your infrastructure consists of various environments each with its own characteristics. If you migrate a workload without understanding the implications of that particular environment, you could risk performance problems or spiking costs. To prevent this, you need any-to-any migration insights to make better decisions about workload priorities, groups and deployments.

    Poor Reservation Planning
    Reservations (i.e., reserved instances or programmatic discounts) offer discounts for committing to a particular plan. If you plan well, the savings can be significant. But if you don’t use the capacity you’ve bought, the savings might not materialize. To improve cloud resource planning, you need to be able to evaluate your potential savings in varied scenarios.

    Workload Drift
    Over time, your workloads evolve due to changing demand, data volumes, etc. As a result, they can outgrow their current configurations, leading to performance issues or rising costs. This is a natural result of business growth, so the goal isn’t to prevent that drift. Instead, you want to optimize capacity and cost on an ongoing basis to keep your workloads right-sized.

    Slow Problem Troubleshooting and Remediation
    No matter how many precautions you take, something somewhere will inevitably go wrong. And once you become aware of the situation, the clock starts ticking to find the cause and fix it, all of which takes time. The longer the entire process takes, the longer your infrastructure will be visibly and painfully not working as expected. For those cases, you need full-stack visibility with application-centric, AI-based analytics to minimize impact duration as much as possible.

    Keeping the engine running behind the curtain can be a daunting task, but with proper planning and management, your IT team will remain visible to those responsible for managing it and invisible to those in the rest of the organization. Of course, the complexity of today’s cloud infrastructure makes that challenging, and there will be IT fire drills that take place, but it’s about how IT leaders react to the situation and resolve it in the quickest way possible, allowing themselves to return to their preferred place in the shadows.

  • Threads: Twitter Killer or Ad-Infested Hellscape?

    Threads: Twitter Killer or Ad-Infested Hellscape?

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Meta launches its much-leaked Threads microblogging app. A spinoff from Instagram, it’s already reached 30 million signups at the time of writing—even before it’s available in the EU. But how is Meta’s DevOps team making it scale? (more…)

  • Cloud Orchestration and its Impact on DevOps Teams

    Cloud Orchestration and its Impact on DevOps Teams

    Cloud orchestration refers to the coordination and management of various interconnected cloud services and resources to automate and streamline complex IT processes. In simple terms, it is the process of integrating, synchronizing, and automating various cloud-based components to create a seamless and unified workflow. 

    Cloud orchestration aims to optimize the deployment, management, and use of resources in a cloud environment, improving efficiency, reducing costs, and ensuring that cloud services are delivered in a timely and effective manner. It is related to the concept of CloudOps (cloud operations), which uses cloud orchestration concepts to allow more efficient management of cloud resources. Cloud orchestration is a critical aspect of modern IT infrastructure management, as it enables organizations to manage and control their cloud resources more effectively.

    Difference between Cloud Orchestration and Cloud Automation

    Though the terms cloud orchestration and cloud automation are often used interchangeably, they have distinct differences. 

    Cloud automation refers to the process of automating individual tasks and processes within the cloud environment. It involves the use of scripts, templates and other tools to automate repetitive tasks, such as provisioning resources, deploying applications and configuring environments. The primary goal of cloud automation is to reduce the time and effort needed to manage cloud resources.

    Cloud orchestration is a broader concept that encompasses not only the automation of tasks but also the coordination and management of the entire cloud ecosystem. It involves the integration of various cloud services, tools and resources to create a unified, streamlined workflow. Cloud orchestration ensures that all components in the cloud environment work together seamlessly, automating complex processes and managing dependencies between tasks.

    In essence, cloud automation is a subset of cloud orchestration. While cloud automation focuses on automating individual tasks, cloud orchestration takes a more holistic approach, orchestrating multiple tasks and resources to achieve a higher level of automation and efficiency.

    How Cloud Orchestration Enhances DevOps Practices

    Increased Efficiency and Speed

    One of the main benefits of cloud orchestration in a DevOps context is the ability to increase efficiency and speed in the software development and deployment process. By automating and streamlining various tasks, cloud orchestration eliminates manual processes, reduces the potential for human error and accelerates the overall development cycle. 

    Improved Collaboration and Communication

    Cloud orchestration fosters better collaboration and communication between development and operations teams by providing a unified platform for managing cloud resources. This shared platform enables teams to work together more effectively, breaking down traditional silos and ensuring that all stakeholders have visibility into the development and deployment process. 

    Enhanced Infrastructure and Application Management

    Cloud orchestration simplifies the management of complex cloud infrastructure by providing a single, unified platform for cloud optimization—improving the management of resources, applications and services in the cloud.

     This centralized management approach enables organizations to more easily monitor, control and optimize their cloud environment, ensuring that resources are used effectively and that applications are running smoothly. With cloud orchestration, organizations can more easily scale their infrastructure to meet changing demands, ensuring that they have the resources they need when they need them.

    Improved Monitoring and Troubleshooting

    Cloud orchestration provides improved monitoring and troubleshooting capabilities, enabling organizations to more effectively identify and address issues in their cloud environment. With comprehensive monitoring tools and dashboards, teams can gain insight into the performance of their applications, identify bottlenecks and pinpoint potential issues before they become critical.

    Challenges of Integrating Cloud Orchestration With DevOps

    Despite the numerous benefits of cloud orchestration, integrating it into DevOps practices can present several challenges. Some of the common challenges include:

    • Complexity: Cloud orchestration involves the integration of various cloud services, tools and resources, which can be complex and time-consuming. Organizations may struggle with complexity, particularly if they lack the necessary expertise or resources to effectively implement and manage the solution.
    • Resistance to change: Implementing cloud orchestration requires a change in mindset and approach to software development and deployment. As your organization migrates more systems to the cloud, some team members may be resistant to adopting new processes and tools, making it difficult to fully embrace and realize the benefits of cloud orchestration.
    • Security concerns: As with any cloud-based solution, security is a critical concern when integrating cloud orchestration into DevOps practices. Organizations must ensure that their solution is secure and that they have the necessary policies and procedures in place to protect their sensitive data and resources.
    • Cost and resource constraints: Implementing and managing a Cloud Orchestration solution can be resource-intensive, requiring significant investment in infrastructure, tools, and personnel. Organizations must carefully consider the costs and resources required to implement cloud orchestration and determine whether the benefits outweigh the investment.

    Best Practices to Integrate Cloud Orchestration Into DevOps

    To successfully integrate cloud orchestration into DevOps practices and overcome the challenges mentioned above, organizations should consider the following best practices:

    • Assess your organization’s readiness: Before implementation, evaluate your organization’s readiness for change. This includes assessing your existing infrastructure, processes and tools, as well as your team’s willingness and ability to adopt new practices and technologies.
    • Develop a clear strategy: Develop a clear strategy for integrating cloud orchestration into your DevOps practices. This should include defining your goals and objectives, identifying the tools and resources you will need and establishing a timeline for implementation.
    • Choose the right tools and technologies: Select the appropriate tools and technologies that best meet your organization’s needs and requirements. Consider factors such as ease of integration, scalability, security and cost when evaluating potential solutions.
    • Train and educate your team: Ensure that your team has the necessary knowledge and skills to effectively implement and manage cloud orchestration. This may involve providing training and education on new tools, processes and best practices, as well as fostering a culture of continuous learning and improvement.
    • Monitor and measure success: Continuously monitor and measure the success of your implementation. This includes tracking key performance indicators (KPIs), such as deployment speed, resource utilization and application performance, to ensure that your solution is delivering the desired results.

    Conclusion

    Cloud orchestration is an essential tool for organizations looking to streamline and optimize their IT infrastructure, particularly in the context of DevOps practices. By automating and coordinating complex cloud processes, organizations can improve efficiency, speed, collaboration and overall performance. 

    However, successfully integrating with DevOps can be challenging, requiring a clear strategy, the right tools and technologies and a commitment to continuous improvement. By following the best practices outlined in this guide, organizations can overcome these challenges and fully realize the benefits.

     

     

  • FinOps Gaining Ground, But Progress is Slow

    FinOps Gaining Ground, But Progress is Slow

    A CloudBolt Software survey found that while the majority (98%) of respondents worked for organizations with a formal FinOps team (82%) or are actively considering adding one (16%), realizing value from FinOps is slow. More than half of existing FinOps teams (54%) were formed just 12 months ago or less, the survey also found.

    The trouble is, only one in of 500 respondents claimed to achieve any material value from FinOps to date. More than three-quarters of respondents (76%) said they will have to wait two to three years to see real FinOps-driven results.

    The survey polled 500 executives, engineers and developers at companies with over 5,000 employees in the U.S., Canada, United Kingdom and Australia. It was conducted by Wakefield Research on behalf of CloudBolt Software, a provider of a platform for managing distributed computing environments.

    A total of 45% of respondents also questioned FinOps efficacy in the real world. Specifically, 15% flagged FinOps as being “theoretically easy but harder in practice,” with another 13% regarding it as “much ado about nothing.” Another 10% said it is “nothing more than a suggested framework,” while 7% labeled FinOps as “a necessary evil.”

    Nevertheless, a full 89% also described FinOps as the silver bullet for reining in the complexity of cloud cost management, with 68% noting that FinOps is among their organization’s most strategic priorities for 2023. The average size of a FinOps team is now 4.1 people, with the “director level” being the senior-most leader on the team 53% of the time, the survey noted.

    A total of 71% noted that achieving this year’s IT goals without a FinOps practice would pose a significant challenge, with 74% reporting that FinOps is now on par with ITOps, DevOps, SecOps and other IT disciplines.

    In addition, 71% of respondents said funding for FinOps initiatives increased in 2023, with 58% reporting key performance indicators (KPIs) and metrics to the C-suite or board of directors.

    CloudBolt Software CTO Kyle Campos said it’s clear that when it comes to reining in costs, large enterprises are struggling to effectively manage IT across multiple silos. In many cases, organizations were investing heavily in IT before and during the COVID-19 pandemic to drive digital business transformation initiatives without many cost controls being put in place, he added.

    Now that organizations are encountering economic headwinds, many chief financial officers are now asking IT leaders to be a lot more efficient, noted Campos.

    Developers, in particular, have for years been provisioning cloud infrastructure resources with little to no oversight. Unfortunately, developers are prone to leaving the proverbial lights on by, for example, leaving application development environments running over a weekend or simply forgetting that a virtual machine has been left to run for weeks—sometimes even months.

    Developers are also not especially good at alerting IT leaders of possible spikes in consumption due to, for example, a data-intensive workload being constructed. No finance team likes to be surprised by a monthly bill for a cloud service that is twice as much as the previous month.

    The one thing that is certain is that time is of the essence. FinOps, at its core, may not necessarily be a new idea, but IT organizations that fail to practice it may soon find themselves being held more accountable than any time in recent memory.

  • Applitools Acquires Preflight to Further Apply AI to App Testing

    Applitools Acquires Preflight to Further Apply AI to App Testing

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

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

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

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

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

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

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

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

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

  • Survey Surfaces Raft of Enterprise IT Challenges

    Survey Surfaces Raft of Enterprise IT Challenges

    A survey of  enterprise IT leaders found less than a third (32%) are extremely confident that they have the right technology, processes and people in place to execute an effective approach to IT management.

    The survey, conducted by Rocket Software, polled 275 U.S. IT leaders in organizations with more than 1,000 employees. A full 93% of IT leaders are also committed to maximize the value of investments in existing platforms.

    As a result, the top priorities for IT leaders are hybrid cloud computing (65%), data and content management (68%) followed by DevOps and infrastructure and application modernization, tied at 58%, and automation (57%).

    A total of 62% noted they have a much greater focus on efficiency due to the economic landscape, with faster DevOps processes, automated processes and increasing overall output as the top three improvements that would be most impactful for increasing efficiency.

    The survey also suggested many organizations still have much work to do to achieve those goals, with 63% of respondents spending anywhere from six to 15 hours on manual data entry and analysis each week.

    Phil Buckellew, president of infrastructure modernization for Rocket Software, said most organizations are still trying to determine the right level of DevOps to adopt. Few organizations have implemented DevOps workflows on an end-to-end basis, with many employing a mix of DevOps and waterfall processes to build and deploy applications across a hybrid cloud computing environment, he noted.

    Overall, IT leaders are most worried about improving overall IT performance (60%), data security (50%), process risk and compliance (46%) and the need to improve agility (41%), the survey found. Respondents also noted that they measure success within their IT organization by increased efficiency (71%), optimized resources (67%) and reduced risk (63%).

    The survey makes it clear that, despite challenges, enterprise IT organizations are committed to hybrid cloud computing as they become more comfortable with using various platforms that are fit for purpose, said Buckellew.

    Mainframes, for example, continue to play a major role in running online transaction processing (OLTP) applications alongside analytics applications that are now being infused with artificial intelligence (AI) capabililities, noted Buckellew.

    A quarter of survey respondents also identified providing cloud applications with access to mainframe data as an area where they need additional tools, followed by automation (22%).

    The biggest automation obstacles are the complexity of implementing automation (18%), resistance to process change among workforce (18%) and lack of skillsets to implement automation (18%), the survey finds.

    As always, the biggest challenge in enterprise IT environments is often simple inertia. Organizations that have been building and deploying applications on mainframes, for example, have been relying on waterfall processes for decades. Many of them have now adopted DevOps processes to varying degrees, but few have completely replaced legacy approaches to building and deploying software, noted Buckellew.

    It’s not likely the management of complex hybrid cloud computing environments will be simplified any time soon, but progress continues to be made. Soon, AI may automate many existing manual tasks. In the meantime, however, the number of enterprise IT organizations standardizing on a single IT platform remains few and far between.