devopsy.kinsta.cloud

Author: Mike Vizard

  • 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.

  • ServiceNow Launches Incident Management Platform Infused with Observability

    ServiceNow Launches Incident Management Platform Infused with Observability

    ServiceNow added an incident management platform based on the Lightstep observability platform it acquired last year to its software-as-a-service (SaaS) portfolio.

    Ben Sigelman, general manager of Lightstep at ServiceNow, said Lightstep Incident Response gives DevOps teams access to observability tools that, via a self-service portal, enable them to determine the root cause of any incident much faster. It combines the ability to orchestrate on-call escalation, alert grouping, incident analysis and remediation with a set of collaboration and incident management tools.

    That approach will make the Lightstep observability platform much more accessible via a cloud service, noted Sigelman. The Lightstep observability platform itself is based on a time-series database capable of processing one trillion events each day.

    Lightstep Incident Response manages an organization’s on-call rotations by synchronizing schedules via a shared calendar, with specific tags that indicate who needs to be looped in based on the nature of the incident and the service that is impacted. Team members are invited to a dedicated channel based on prebuilt collaboration integrations for quick remediation. Over time they can create automated processes that self-triage and self-remediate problems as they reoccur. The platform also integrates with monitoring, observability and collaboration tools such as LogicMonitor, Postman, Slack, Sumo Logic and Zoom.

    Lightstep Incident Response is generally available now in both free and paid versions. Pricing is based on the use of active services being managed rather than by seat license.

    Lightstep Incident Response also natively integrates with the Now Platform that ServiceNow created for IT service management (ITSM). That integration will enable organizations to more easily meld ITSM and DevOps workflows, noted Sigelman.

    Observability platforms aggregate the collection of logs, metrics and traces in a way that makes it possible for DevOps teams to query that data. The goal is to make it easier for DevOps teams to identify anomalies that might disrupt an application environment. Those same tools, however, also allow DevOps teams to identify the root cause of an IT incident much faster. Today many organizations routinely convene “war rooms” that require IT teams to painstakingly identify the root cause of an issue by a process of elimination; this can take days—sometimes even weeks—to complete.

    With more applications becoming instrumented—thanks, in part, to the availability of open source agent software—Sigelman said observability platforms will play a critical role in helping to automate incident management processes. Prior to being acquired by ServiceNow, Lightstep team members played a role in the creation of both the OpenTracing and OpenTelemetry open source projects for collecting traces, metrics and logs. Today, OpenTelemetry is a sandbox-level project being managed under the auspices of the Cloud Native Computing Foundation (CNCF).

    With the deployment of more microservices-based applications, the percentage of applications that are instrumented will soon increase substantially. It’s not feasible to manage these highly distributed applications without some level of instrumentation. The issue now, of course, is determining to what degree to also instrument many of the monolithic applications that are already running in those same environments.

  • 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.

  • Pulumi Adds Business Critical Edition to Infrastructure Management Platform

    Pulumi Adds Business Critical Edition to Infrastructure Management Platform

    Pulumi today added a Business Critical edition of its Cloud Engineering Platform that provides large enterprises with additional security and compliance features, premium 24×7 support, training, professional services and a self-hosting option.

    In addition, the company announced that Pulumi Enterprise and Business Critical editions are now available via the online marketplace run by Amazon Web Services (AWS), with future availability on other marketplaces planned.

    The Cloud Engineering Platform from Pulumi enables DevOps teams to use Pulumi Packages to create reusable components for automating IT infrastructure provisioning. Those can be embedded within workflows and applications using an application programming interface (API) that Pulumi has exposed. IT teams can define a Pulumi Package in the programing language of their choice.

    Aaron Kao, vice president of marketing for Pulumi, said the Cloud Engineering Platform from Pulumi provides an alternative to managing infrastructure-as-code (IaC) that can be centrally managed more easily than open source tools like Terraform.

    Pulumi reported that adoption has accelerated across enterprises around the world with more than one million downloads per month. The company claimed it has seen 350% year-over-year growth of its enterprise customers, such as Mercedes-Benz, in the last 12 months.

    IT organizations in general are looking to bring some order to chaotic provisioning processes to avoid misconfigurations that often result in, for example, a developer leaving a port open; that open port leaves a vulnerability through which data can be exfiltrated.

    As organizations focus more on software supply chains in the wake of recent high-profile breaches, it’s only a matter of time before more questions arise about how cloud infrastructure is being provisioned. Much of the concerns organizations already have about cloud security don’t stem from the platforms themselves. Rather, it’s the processes employed to provision infrastructure under a shared cloud security responsibility model that results in so many vulnerabilities. Developers often assume the cloud service provider is securing configurations, only to discover later that it was their own responsibility to validate those configurations. Security teams, meanwhile, can’t keep pace with the rate at which cloud infrastructure resources are being provisioned.

    Centralizing infrastructure management is especially important as the number of cloud platforms IT organizations manage continues to expand and the rate at which applications are built and deployed shows no sign of slowing, noted Kao.

    It’s not clear whether development or IT operations teams will ultimately wind up managing the provisioning of infrastructure. Pulumi is making a case for a platform that enables those teams to more closely collaborate within the context of a set of policies and DevSecOps best practices that the IT organization defines.

    It may be a while before the bulk of enterprise IT organizations are able to exercise the political will to regain control over IaC provisioning. However, in the absence of guardrails that allow IT organizations to make sure infrastructure is properly configured, it’s only a matter of time before tougher questions are asked about how software supply chains are managed.

  • Tabnine, GitLab Build Custom Apps Faster With AI

    Tabnine, GitLab Build Custom Apps Faster With AI

    Tabnine today revealed it has integrated its code completion tool with the GitLab continuous integration/continuous delivery (CI/CD) platform. Tabnine’s platform employs artificial intelligence (AI) to accelerate application development.

    Brandon Jung, vice president of ecosystems and business development at Tabnine, said the goal is to make it easier for developers to automatically write code based on custom models created using AI. Those models would reside in the CI/CD platform provided by GitLab. Developers can now create custom models of their private source code, he noted.

    The Tabnine AI assistant can then be trained on a customer’s private code, style and security best practices that have been defined by an internal DevOps team, added Jung. That assistant, accessible from within a developer’s integrated development environment (IDE), is conversant in 35 programming languages.

    The code completion tool doesn’t eliminate the need for developers to write code; instead, it completes code once developers start writing it based on the model stored in the CI/CD platform.

    Nima Badiey, vice president of global alliances for GitLab, said the overall goal is to increase developer productivity at a time when organizations are launching more application development initiatives than ever. Many of those applications are being constructed using multiple programming languages that each individual developer may not have equally mastered.

    In addition, an AI model will, over time, improve the quality of the code being created, noted Badiey. That should make it easier to review code that is more accurate because it was created with the aid of a machine, he added.

    In general, GitLab has been moving to incorporate AI within DevOps workflows. The company last year acquired UnReview, a provider of a tool that uses machine learning algorithms to identify which code reviewers to assign to a project based on both the quality of their previous efforts and their current workloads.

    It’s not clear to what degree AI will be pervasively embedded within DevOps workflows. However, it’s now just a matter of time before algorithms are more widely employed. Organizations that employ DevOps today devote significant resources to maintaining platforms that could be simpler to manage if more tasks and processes were automated. In theory, more resources could be then devoted to building applications versus maintaining DevOps platforms.

    One way or another, more organizations are moving to increase developer productivity. Developers are both hard to find and retain, so organizations have a vested interest in finding ways to increase the rate at which an existing pool of developers can build applications capable of driving a wider range of digital business transformation initiatives. Of course, an increase in the rate at which applications are developed also has significant implications for the rate at which they might need to be deployed.

    Not everyone in IT is, of course, entirely comfortable with AI. Nevertheless, most DevOps organizations are highly committed to automating as many processes as possible. The only thing that will be different going forward is determining to what degree those processes will be automated by humans versus a machine.

  • Linux Foundation Lists Top Open Source Libraries

    Linux Foundation Lists Top Open Source Libraries

    The Linux Foundation today published a report that provides access to eight lists of the top 500 open source libraries being used by organizations as part of an ongoing effort to help better secure software supply chains.

    The Census II of Free and Open Source Software—Application Libraries report is based on usage data from providers of software composition analysis (SCA) tools such as Snyk, the Synopsys Cybersecurity Research Center (CyRC) and FOSSA. The report itself was compiled by the Harvard Lab for Innovation Science.

    The eight lists are made up of four that include version numbers and four that are version agnostic. The lists are separated into npm and non-npm packages because npm packages would dominate any ranking created. The top non-npm packages being employed include maven, nugget, Go and cargo.

    Frank Nagle, assistant professor at Harvard Business School, said the report is intended to provide organizations with some guidance in terms of knowing what open source packages are used in the wake of recent vulnerabilities discovered in the widely employed Log4j software used to manage logs within Java applications.

    Many of the organizations impacted by that vulnerability were unaware of just how widely deployed Log4j was within their enterprise IT organizations. IT organizations should evaluate the sustainability of those projects in terms of the number of contributors and maintainers that are working to ensure the security of those libraries.

    One of the biggest challenges is to find a way to standardize naming schema for software components and the overall management of versions of different libraries, noted Nagle.

    Ultimately, the goal is to convince more enterprise IT organizations—and the vendors that support them—to make more resources available to secure those libraries, noted Nagle. Much of the most widely used open source software is developed and maintained by only a handful of contributors. Many of those contributors argue that, while they made that software available for free, it is the responsibility of the organizations employing it to secure it.

    The Linux Foundation is moving to become a conduit through which it will make available additional resources to help better secure open source software. The Open Source Security Foundation (OpenSSF), a consortium hosted at the Linux Foundation, earlier this week announced that 19 additional organizations have joined OpenSSF to help identify and fix security vulnerabilities in open source software and improve tooling, training, research, best practices and vulnerability disclosure practices.

    Brian Behlendorf, executive director at OpenSSF, said in addition to making resources available, it’s clear there is a need for some form of a third-party audit of open source software as organizations are more broadly employing that software across their software supply chains.

    New premier member commitments to OpenSSF are now coming from 1Password, Citi, Coinbase, Huawei Technologies, JFrog and Wipro. New general member commitments are being made by Accuknox, Alibaba Cloud, Block, Inc., Blockchain Technology Partners, Catena Cyber, Chainguard, DeployHub, Gravitational Inc., MongoDB, NCC Group, ReversingLabs, Spotify and Wingtecher Technology. New associate members include Institute of Software, the Chinese Academy of Science (ISCAS), MITRE and OpenUK.

    Other efforts led by the OpenSSF include an Alpha-Omega Project to better secure open source security posture, scorecards that identify risks of the dependencies within more than one million projects, multi-factor authentication tokens and a Project Sigstore initiative to sign, verify and protect open source code.

    It may be a while before open source software achieves the level of security the IT industry strives for. On the plus side, however, there have never been more resources allocated to achieving that goal.

  • Multi-Cloud Computing is a Major IT Headache

    Multi-Cloud Computing is a Major IT Headache

    A CloudBolt Software survey of 500 senior IT, DevOps, security and finance leaders found 79% are struggling under the weight of managing multiple cloud computing environments.

    The survey from CloudBolt Software, a provider of a cloud management platform, found specifically that more than three-quarters of respondents (78%) said they have too many cloud tools throughout their enterprise, and 80% struggled to achieve comprehensive visibility into cloud usage and spending. A total of 80% also noted that their current approach to cloud computing created new security vulnerabilities.

    A full 88% said they need a “manager of managers” that would enable them to unify the various silos that have multiplied across their IT environment.

    Nevertheless, 78% of respondents said that moving to the cloud enabled them to achieve cost savings compared to running applications in an on-premises IT environment.

    CloudBolt Software CEO Jeff Kukowski said the issue is not so much that IT organizations regret shifting workloads to the cloud; instead, the issue is finding a way to manage multiple clouds, each with their own set of tools and frameworks that need to be mastered.

    It’s not clear whether organizations are deliberately employing multiple clouds or if, because of simple inertia, they have found themselves managing multiple cloud platforms because of decisions made by various development teams. Some IT organizations, for example, employ multiple clouds because they are concerned about either becoming locked into a specific cloud computing service or whether an outage might take all their applications offline at the same time. Others are simply trying to take advantage of services that are better provided by one vendor than another.

    Regardless of how IT organizations found themselves managing multiple clouds, the total cost of IT rises as each new platform is added to an IT environment. CloudBolt Software is making a case for an IT framework that provides a layer of abstraction through which the management of multiple clouds and on-premises IT environments can be unified. It is more a question of time and degree to which IT organizations will eventually consolidate the management of disparate IT environments under a single control plane that provides the foundation for true hybrid cloud computing, said Kukowski.

    There are, of course, a few organizations that have standardized on a single cloud computing platform. The degree to which those organizations can maintain that standard will naturally vary as, for example, new workloads that may be optimized for a specific cloud environment are built or as companies acquire other organizations that are running workloads on other cloud platforms. In many of those cases, organizations discover that the cloud services invoked expose a range of proprietary application programming interfaces (APIs) and disparate virtual machines that make migrating workloads a major challenge.

    Like it or not, most organizations will be managing multiple IT environments for the foreseeable future. The only thing that is uncertain is the volume of workloads that might be running on any one of those platforms at any given time.

  • Tricentis Expands Scope of Cloud-Based Testing Tools

    Tricentis Expands Scope of Cloud-Based Testing Tools

    Tricentis this week extended a suite of cloud-based testing tools that promise to make it simpler to both develop cloud-native and mobile applications and migrate existing applications to the cloud.

    New capabilities added to the portfolio include integrations for cloud-based planning tools and a wider range of testing capabilities for mobile applications. DevOps teams can now, for example, deploy Tricentis qTest platform for managing the testing process within a Kubernetes cluster. Tricentis has also made it simpler to configure and update its Tosca suite of application testing tools.

    In addition, Tricentis added additional capabilities for testing SAP applications running in the cloud that previously were deployed in an on-premises IT environment.

    Martin Klaus, vice president of product marketing for Tricentis, said the goal is to make it simpler for DevOps teams to integrate continuous testing for various types of applications within their DevOps workflows. DevOps teams have the option of invoking the Tricentis suite of testing tools via a software-as-a-service (SaaS) platform or deploying them on a cloud platform themselves using templates provided by Tricentis.

    While developers are clearly getting more involved in testing, Klaus said it’s critical for testing capabilities to be integrated within a DevOps pipeline. Each developer is typically only working on a component of a larger application. Unless testing is conducted on an end-to-end basis, an organization won’t be able to determine the real quality of the application experience before deploying software in a production environment, he noted.

    As a result, the decision about what tools to use as part of a continuous testing process is now being made by DevOps team leaders rather than individual developers, added Klaus.

    Ultimately, the goal is to automate as much of the continuous testing process as possible. Achieving that goal will require increased reliance on artificial intelligence (AI) technologies, though to varying degrees, said Klaus. Those capabilities, however, are not likely to eliminate the need for dedicated testing professionals; instead, AI will enable those teams to more efficiently test increasingly complex application environments, he added.

    In fact, it’s becoming practically impossible for testers to manually inspect every element of an application without the help of, for example, algorithms that apply computer vision to analyze a user interface, noted Klaus. There will always be higher-level tasks that require a human, but AI technologies will play a significant role in enabling testing at scale as organizations start to build more applications, he added.

    In the meantime, it’s clear that there’s a lot more riding on testing as organizations continue to launch digital business transformation initiatives. In many cases, organizations only get one chance to convince end users that a new application being rolled out is better than the processes they previously employed. As such, the need to streamline testing within the context of a DevOps workflow has become critical—there is little tolerance for bottlenecks that slow down the rate at which applications now need to be built and deployed.