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Category: News

  • Rocket Software Extends DevOps Reach to IBM I Platforms

    Rocket Software Extends DevOps Reach to IBM I Platforms

    Rocket Software has added support for the IBM I platform as part of its relaunch of a DevOps platform that also runs on IBM mainframes.

    Chris Wey, president of the data modernization business unit at Rocket Software, said the Rocket DevOps continuous integration/continuous delivery (CI/CD) platform will make it possible for organizations that employ both IBM I midrange platforms and mainframes to modernize application development and deployment using a common framework.

    Modernization of application development on mainframes has been occurring for some time. A survey of more than 500 U.S. IT professionals conducted by Rocket Software found 44% of respondents employ multiple DevOps tools, but slightly less than a quarter (24%) have a comprehensive DevOps platform in place.

    Rocket Software is expanding its DevOps reach to include the IBM I platform because organizations that rely on this platform have the same pressing need as other organizations to more quickly build applications, said Wey.

    It’s not clear to what degree organizations are still building new applications for what have become venerable IBM platforms. A Rocket Software survey found that while 82% of respondents are migrating workloads to the cloud, only 4% reported they are migrating completely to the cloud. The bulk of most mission-critical applications running on mainframes are online transaction processing (OLTP) applications that require a level of processing capability that still can’t be easily matched using any other platform. There are plenty of instances where certain classes of workloads have been migrated off the mainframe to run on lower-cost platforms. However, there are still many enterprise applications that continue to run faster on mainframes than they could on any other platform.

    In most cases, Wey noted, organizations will be deploying and updating applications across hybrid cloud computing environments for years to come. The immediate challenge they face is a lack of DevOps skills among the IT teams that manage IBM I systems and mainframes, he added.

    Rocket Software also provides access to a set of application development tools for building modern applications on IBM platforms. Most recently, the company added support for an open source Python software development (SDK) that IBM created for the Python programming language to the Rocket Open AppDev for Z framework for building mainframe applications.

    The overarching goal is to make IBM platforms more accessible to a wider range of application developers and DevOps professionals that today mainly work on distributed computing platforms, noted Wey.

    One way or another, the divide that has existed between legacy platforms and distributed computing systems is continuing to narrow. Many organizations are trying to lower the total cost of IT by centralizing management of both these classes of systems versus continuing to hire and retain separate IT teams.

    The IBM I platform traces its lineage back to AS/400 systems that were first launched in 1988 as an integrated midrange minicomputer rival to VAX/VMS systems from Digital Equipment Corporation (DEC). Fast-forward more than 30 years later and there still are many IBM I platforms running long after VAX/VMS systems have all but disappeared.

  • Harness Acquires ChaosNative to Meld Chaos Engineering, DevOps

    Harness Acquires ChaosNative to Meld Chaos Engineering, DevOps

    Harness this week announced it acquired ChaosNative as part of a plan to more deeply integrate chaos engineering with DevOps workflows.

    At the same time, the company added a Harness Service Reliability Management (SRM) and Harness Security Testing Orchestration capability to its continuous integration/continuous delivery (CI/CD) platform.

    Chaos engineering, as a discipline, refers to experiments on a software system that test its resiliency in the face of an unplanned outage. The goal is to deliberately make resources unavailable to see how the application environment either adjusts or whether it fails.

    ChaosNative’s LitmusChaos platform, which manages these chaos engineering tasks, was donated to the Cloud Native Computing Foundation earlier this year.

    Scott Sanchez, chief marketing officer for Harness. said the company plans to shift chaos engineering further left as part of an effort to enable developers to test the resiliency of their applications before they are deployed in a production environment.

    It’s still early days where adoption of chaos engineering within DevOps workflows is concerned. Many IT leaders’ performance is measured based on the stability of application environments. The idea that they should deliberately break something as part of an effort to make an application more resilient requires a shift in mindset, noted Sanchez.

    However, once IT leaders start to appreciate how chaos engineering actually improves the availability of applications it’s only a matter of time before the practice becomes more widely implemented, he added.

    In the meantime, Harness is looking to automate more of the lower-level tasks that today’s DevOps teams often manually manage. Harness SRM enables DevOps teams to automatically track service level objectives (SLOs) that are attached to specific pipelines within the Harness CI/CD platform.

    The Harness STO module, meanwhile, makes it simpler to orchestrate security testing across multiple pipelines.

    In general, there is now more focus on the productivity of DevOps teams as the number of applications that are being built simultaneously increases. The challenge, however, is not just the number of applications but also the dependencies that exist between them.

    Harness is making a case for a CI/CD platform that automates DevOps tasks by employing, for example, machine learning algorithms to identify which applications are most likely to fail before a build is completed. The goal is to reduce the amount of time DevOps teams waste waiting for builds to complete only to discover that the application almost immediately failed.

    The time, effort and cost of maintaining a DevOps platform will naturally vary as the overall environment becomes more complex. However, as DevOps processes become more automated, the platforms themselves become more accessible to a wider range of organizations. Today, the effort required by a DevOps to set up and then support a CI/CD environment is often considerable.

    Nevertheless, as more organizations rely on custom applications to drive digital business processes, it’s only a question of when and to what degree DevOps best practices will be more widely adopted. After all, the total cost of application downtime is only going to rise in the age of digital transformation.

  • Apple Outage Outrage | Linux Random Redo | Okta Hacked (or Not)

    Apple Outage Outrage | Linux Random Redo | Okta Hacked (or Not)

    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: Why Apple services were down, Linux gets a huge RNG overhaul, and we wonder if Okta was hacked again. (more…)

  • Survey Sees App Testing More Widely Distributed

    Survey Sees App Testing More Widely Distributed

    A survey of 900 application testing professionals conducted by PractiTest, a provider of a platform for managing testing tools, suggested the number of organizations that have dedicated testing teams is declining at a steady rate.

    The survey found a 15% decrease in organizations with dedicated testing teams that have six or more members. At the same time, however, the survey found only 18% of respondents reported that all testing is done by individuals that are not part of a dedicated testing team.

    Joel Montvelisky, test specialist and chief solution architect at PractiTest, said that shift indicates that more organizations are steadily incorporating testing within DevOps workflows. In fact, more than 60% of respondents said DevOps and Agile processes led to teams being more organized and stable, with two-thirds (66%) reporting that development teams were releasing features more frequently with more functionality.

    However, only 38% of respondents said they were active participants in a continuous integration/continuous delivery (CI/CD) workflow. A full 90% said they needed to coach or train other members within the team and just over half (54%) reported that estimations of the time required for development and testing were accurate.

    Two-thirds also said collaboration with developers is also improved when applying DevOps processes, with more than half reporting fewer serious bugs made it into production. More than half also said they monitor software in production and deployment environments. The number of testers who defined themselves as engineers has gone up by 14% year-over-year. However, only 44% report that automation has replaced 50% or more of their manual testing efforts.

    A full 95% or more of survey respondents identified application programming interface (API) testing, Agile methodologies, functional testing and scripting as high on their list of priorities. In contrast, big data testing, internet of things (IoT) testing and machine learning/artificial intelligence (AI) testing were cited as important or very important by only 60% of respondents.
    Finally, about a third of survey respondents noted that non-testers are now involved in testing activities.

    Montvelisky said the PractiTest survey makes it clear that while developers are a little more involved in testing than they were a few years ago, organizations still relied on testing specialists that are becoming more integrated into DevOps workflows. This is especially important as more organizations launch digital business transformation initiatives that require them to build software using the same types of DevOps processes that independent software vendors (ISVs) employ.

    It’s still too early to say just how far testing might shift left toward developers, but as testing becomes more automated—thanks, in part, to increased reliance on AI—the types of tests that will be conducted by a dedicated testing team will evolve. In fact, it’s unlikely automation will eliminate the need for those teams any time soon. Rather, testers will be augmented by AI technologies that will help them to keep pace with the accelerated rate of application development enabled by wider adoption of DevOps best practices within organizations both large and small.

  • CSS-Tricks Sells Out | Peloton Spins Out | ARM Axes 1,000

    CSS-Tricks Sells Out | Peloton Spins Out | ARM Axes 1,000

    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: CSS-Tricks gets bought by DigitalOcean, Peloton pedals while its business burns, and Arm lays off up to 15% of staff. (more…)

  • Log Management Challenges Grow With Observability Adoption

    Log Management Challenges Grow With Observability Adoption

    It appears that, in many IT environments, observability is becoming too much of a good thing. A global survey of more than 315 IT executives, cloud application architects, DevOps professionals and site reliability engineers (SRE) conducted by Dimensional Research on behalf of log data management platform provider Era Software, found 96% are witnessing an explosion of log data.

    The survey also found 79% of respondents reported the overall cost of observability and log management will skyrocket in 2022 if current tools don’t evolve.

    More than three-quarters of respondents (78%) also noted attempts to manage volumes of log data have had mixed or unwanted results such as an inability to access data. Nearly two-thirds (65%) are also evaluating their observability options while the other 41% are considering it.

    Stela Udovicic, senior vice president of marketing for Era Software, said the survey makes it clear that as organizations rely more on logs to analyze IT events, the cost of storing all that log data is becoming a significant challenge. Overall, the survey finds usage of observability tools and platforms has jumped by 180% as IT teams struggle to manage increasingly complex IT environments.

    On the plus side, more business users are benefiting from log data. A full 83% of respondents report that business stakeholders outside of IT use insights from log data, with 96% reporting log data is being used to solve business problems.

    Observability has always been a core tenet of DevOps best practices, but achieving it has always been a challenge. Monitoring tools are designed to consume predefined metrics to identify when a specific platform or application is performing within expectations. The metrics tracked generally focus on, for example, resource utilization. However, whenever there is an issue, it can still take days, sometimes weeks, to discover the root cause of an issue through what amounts to a process of elimination.

    In contrast, observability combines metrics, logs and traces—a specialized form of logging—to instrument applications in a way that makes it simpler to troubleshot issues without relying solely on a limited set of metrics that have been predefined to monitor a specific process or function. DevOps teams can employ queries to interrogate data in a way that makes it easier to discover the root cause of an issue. Observability platforms correlate events in a way that makes it easier for analytics tools to identify anomalous behavior indicative of an IT issue.

    The issue that arises is that IT teams need to find a way to store all the data generated by the logs they need to analyze. Each IT team can, of course, could reduce the amount of log data they retain. However, there is always a concern that in the event of an incident critical log data won’t be available.

    Udovicic said most IT organizations have historically viewed storing log data as a necessary evil. The primary issue most of them encounter is the simple fact that log data is not easy to work with, she noted.

    Regardless of how organizations view log data, the amount of it that needs to be managed will only increase as DevOps workflows become more mature. The issue IT teams need to come to terms with now is finding a way to minimize the cost of storing it all.

  • Survey Surfaces Observability Challenges in the Cloud Era

    Survey Surfaces Observability Challenges in the Cloud Era

    A recent cloud observability survey found nearly two-thirds (64%) of respondents reported the adoption of public clouds has made it more difficult to achieve observability. Nearly half of respondents (48%) said they now consider providing real-time insights into application/infrastructure environments to be a top priority.

    The survey polled 357 IT professionals and was conducted by Enterprise Strategy Group (ESG) on behalf of Yotascale, a provider of cost optimization tools for cloud computing environments.

    Yotascale CEO Asim Razzaq said that attaining and maintaining observability becomes more challenging with each new cloud platform adopted. The survey found that 71% of organizations now rely on more than three cloud infrastructure providers.

    A full 84% of survey respondents also noted that tracking costs and cloud cost allocation is burdensome and time-consuming, and 61% admitted they lacked sufficient visibility into which teams own what cloud resources. Nearly a quarter (24%) are considering making changes to their monitoring and observability practices as infrastructure costs rise.

    Observability has always been a core DevOps tenet, but achieving and maintaining it is challenging. Most DevOps teams today aspire to be able to maintain some level of continuous monitoring. However, as it becomes easier and less costly to instrument applications, interest is rising in observability platforms that simplify the investigation of anomalies indicative of an issue that could disrupt an application environment. As that shift occurs, more organizations will seek to correlate events with costs as part of an effort to rein in the costs of IT, said Razzaq.

    In the immediate aftermath of the COVID-19 pandemic, the number of workloads being deployed on public clouds increased sharply. In theory, many organizations signed enterprise licenses that reduce cloud costs if they consistently consume a certain level of infrastructure resources. In practice, however, individual developer teams are showing a preference for one cloud platform over another based on the attributes of their workloads. As a result, however, it becomes more challenging to not only consistently observe diverse platforms but also to control costs.

    It’s still early days as far as the adoption of observability platforms is concerned, but it’s apparent that as application environments become more complex the plethora of monitoring tools that DevOps teams rely on will need to be streamlined. It’s not uncommon for DevOps teams to have multiple monitoring tools that surface a set of pre-defined but often conflicting metrics. Observability platforms unify logs, metrics and traces in a way that makes it simpler to launch queries to identify the root cause of an issue.

    The rate at which DevOps teams will embrace observability will naturally vary. In 2022, the biggest obstacle might not be the technology as much as it is simply understanding what queries can help DevOps teams better understand the root cause of an IT issue before there is a major disruption. In the long term, it’s expected that machine learning algorithms will leverage the data collected by observability platforms to automatically identify issues that might lead to a disruption long before it actually occurs. Regardless of approach, however, the ability to manage complex IT environments in the months and years ahead will continue to steadily improve.

  • CircleCI Report: Pipeline Optimization Best Practices

    CircleCI Report: Pipeline Optimization Best Practices

    CircleCI today published a report that found most successful software engineering teams have average workflow durations of five to 10 minutes, with any failed run being fixed or reverted in under an hour.

    The report was based on two years of data gathered from more than a quarter-billion workflows and collected from nearly 50,000 organizations using its continuous integration/continuous delivery (CI/CD) platform. The CircleCI 2022 State of Software Delivery Report also found those teams have success rates that are above 90% for the default branch of their application and deploy at least more than once per day.

    Mike Stahnke, vice president of platform for CircleCI, said the report also makes it clear there is no correlation between team size and the level of DevOps success achieved.

    Organizations that focus on optimizing pipelines are also better able to integrate testing within their DevOps workflows, he noted.

    In general, Stahnke said more DevOps teams are relying on cloud-based CI/CD platforms versus opting to deploy and maintain infrastructure themselves. That shift also lowers the barrier to entry for organizations looking to embrace DevOps best practices to accelerate application development, he added.

    Less clear is the degree to which organizations have embraced both CD and CI. While many developers routinely employ CI to accelerate application development, CD has proven to be a more elusive goal. Each platform a DevOps team supports tends to have unique attributes that require them to rely on manual processes to deploy applications. However, as DevOps processes continue to mature, Stahnke said the CI and CD integration will become more seamless. In fact, as the number of applications that organizations build simultaneously increases, the pressure to automate CD will also rise.

    However, it remains to be seen to what percentage of organizations will opt for a tightly integrated CI/CD platform versus separate CI platforms for developers and CD platforms for IT operations teams that are more loosely coupled.

    In the meantime, organizations of all sizes are clearly more focused than ever on developer productivity. In the wake of the downturn brought on by the COVID-19 pandemic, there was a rush to launch a range of digital business transformation initiatives. The issue that many organizations are now encountering is they need to find ways to enable a finite number of developers to simultaneously build and deploy multiple applications with multiple interdependencies. Many organizations are trying to address that challenge by sharing best practices across DevOps teams that have varying levels of maturity. Few organizations have a set of standardized DevOps practices that are consistently employed across every application development team within an enterprise.

    It will be up to each organization to decide whether to compare their level of DevOps expertise to other organizations. Each application development team faces a unique set of challenges. The most important goal is simply to continuously improve at a time when demand for custom applications has never been greater.

  • Survey Finds Widespread Adoption of DevOps in Mainframe Sites

    Survey Finds Widespread Adoption of DevOps in Mainframe Sites

    A survey of IT leaders by the market research firm Global Surveyz found a full 100% of respondents are modernizing their mainframe applications, but the reasons behind and approaches to achieving that goal vary widely.

    The research was conducted on behalf of Model9, a provider of tools for migrating mainframe data, and polled 100 companies that have more than 1,500 employees or more and use mainframes. Nearly three-quarters of respondents (73%) are embracing DevOps best practices to manage mainframe infrastructure, with 61% modernizing their mainframe applications. Just under half (46%) also noted they were outsourcing the management of those operations.

    However, the survey also found that unlocking the business value of stored data and accelerating innovation are among the top 2022 objectives for 42% and 36% of respondents, respectively. Data protection (89%), cybersecurity and resilience (76%) and cost optimization (52%) are the top priorities.

    Tamar Milstein, chief marketing officer for Model9, said that the continued focus on block-and-tackle mainframe management issues suggests there may be a disconnect between IT teams managing these platforms and senior business executives that are looking to drive digital business transformation initiatives.

    Nevertheless, a full 91% of respondents said the mainframe still holds the most critical applications for their business, with 62% reporting more than half of their core business applications on these platforms. The use of cloud computing platforms alongside mainframes is also widespread. A total of 61% have a hybrid multi-cloud computing environment already in place. Only slightly over one-third (35%) are using a single cloud to augment their mainframe. Nearly half (48%) are replicating data to the cloud. However, just over half (51%) are storing more than 50% of their data off the cloud.

    Model9 is making a case for modernizing these environments by making it easier for mainframe applications to access data stored on less expensive platforms to house data. That approach provides all the benefits of the cloud without having to incur the risks and costs of attempting to migrate an entire mainframe application to the cloud, said Milstein.

    Regardless of the approach organizations take toward application modernization, the survey made it apparent just how widespread the adoption of DevOps best practices in mainframe environments is. Many of those organizations are using DevOps practices to add application programming interfaces (APIs) to legacy mainframe applications to make the data residing in those applications more accessible. That approach reduces the technical debt that organizations might otherwise incur as they build and deploy applications on other platforms that might provide similar capabilities to an existing mainframe application.

    While there are plenty of organizations that have migrated away from mainframes, there remains a large base of IT teams still running applications on these platforms. Achieving the same level of transaction processing capabilities enabled by a mainframe in a cloud computing environment isn’t yet feasible. There are, of course, organizations that decided they would no longer like to operate a data center at all, but there’s still a substantial number of organizations that will continue to operate mainframes well into the rest of this century.

  • CircleCI Acquires Ponicode to Gain AI Testing Tool

    CircleCI Acquires Ponicode to Gain AI Testing Tool

    CircleCI today revealed it has acquired Ponicode to gain control over a unit testing platform infused with artificial intelligence (AI) that makes it simpler to review application code prior to uploading it into a build. Financial terms of the deal were not disclosed.

    Jean-Philippe (JP) Leblanc, senior vice president of engineering at CircleCI, said the goal is to integrate Ponicode’s testing technology within CircleCI’s continuous integration/continuous delivery (CI/CD) platform. That approach will make it possible to apply algorithms to individual developers’ code as well as to code aggregated from multiple developers within a build as it is continuously updated across a DevOps workflow, he added.

    Ponicode was initially launched in June 2020 as a JavaScript unit testing extension for Microsoft VS Code. Since then, the company has started to support TypeScript, Java and Python, including complex functions such as variable mocking and infer suggestions of complex structures for nested objects.

    The issue that DevOps teams frequently encounter is that even when individual developers test their code prior to uploading into a build, integrating different developers’ code can still result in an application failure. The Ponicode AI testing tools will give both developers and IT operations teams more confidence in the quality of the code being employed to drive applications, noted Leblanc.

    Although not many developers want to admit it, Leblanc noted there is still a lot of sloppy code finding its way into applications.

    In addition, Leblanc said the Ponicode acquisition will help the code review process become more efficient. Instead of requiring another developer or dedicated tester to review code, he said most issues will be surfaced by the AI testing tool that provides the equivalent of a built-in peer review. As a result, AI testing tools will improve the overall quality of the code being used to drive applications, added Leblanc. Developers will also be able to spend more time on the business logic that drives their application than on code reviews.

    The goal, however, is not to eliminate the need for dedicated testing teams as much as it is to augment them and the developers they work with, said Leblanc.

    It’s not clear to what degree responsibility for testing code is shifting left toward developers. However, many developers would prefer to have issues surfaced via an AI tool so they could address issues on their own rather than having them discovered by someone else. In fact, there are already more than 20,000 users of the Ponicode AI tool that plugs directly into developers’ integrated development environment (IDE), said Leblanc.

    In general, there’s never been a greater focus on developer productivity. Organizations are attempting to simultaneously build and deploy more applications than ever—thanks mainly to digital business transformation initiatives launched in the wake of the COVID-19 pandemic. The challenge they face is recruiting and retaining developers. As a result, organizations are looking for ways to increase developer efficiency by eliminating as many manual processes as possible.