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Category: Continuous Testing

Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate.

Bitrise Tightens Mobile DevOps Integration With AWS Cloud Services

Bitrise this week will tighten integration with its DevOps platform for building and deploying mobile applications and the application development services provided by Amazon Web Services (AWS) cloud.

Announced at the AWS re:Invent 2023 conference, the goal is to provide easier access to automated building, testing and release management capabilities provided by AWS as part of an effort to increase overall efficiency.

Bitrise CEO Barnabas Birmacher said those capabilities will streamline workflows for a Bitrise Mobile DevOps platform that has been specifically designed for building and deploying mobile applications that typically need to be updated frequently.

Based on a continuous integration/continuous delivery (CI/CD) platform developed by Bitrise that can be deployed in an AWS cloud computing environment, the integrations will make it simpler to build and run mobile applications at scale, noted Birmacher.

The goal is to reduce the total cost of building mobile applications while at the same time improving developer productivity for mobile applications that have to be thoroughly tested before being made accessible via an application store, he added.

It’s not clear how many organizations will prefer to have a dedicated CI/CD platform for building and deploying mobile applications versus using the same CI/CD platform they might already be relying on to build and deploy other applications. Bitrise claims to have more than 6,000 customers, including Reddit, WISE, Equinox and Philips Hue.

As the percentage of mobile applications that make up an organization’s portfolio continues to increase, the pace at which those applications need to be updated lends itself better to a CI/CD platform optimized for those types of applications, said Birmacher.
In fact, a recent survey conducted by Bitrise found 62% of respondents have been adversely impacted by manual processes that slowed the rate at which mobile applications were deployed and updated. The survey also found (44%) of respondents reported their release approval process is mostly or entirely manual, with only 9% having fully automated application releases.

The survey also found that nearly two-thirds of organizations (66%) have deployed applications that can’t be opened in two seconds or less, which is widely considered the accepted level of performance required for mobile applications. In addition, three-quarters of organizations (75%) required more than two days to address bug fixes.

Many mobile apps have become increasingly mission-critical thanks to digital business transformation initiatives. Consequently, the amount of focus on them within most organizations is high; any issues that arise are likely to be noticed at the highest echelons of an organization. That level of attention can easily put pressure on DevOps teams, mainly because the general expectation among end users is that mobile applications will be regularly updated with new functionality. However, if release processes are largely manual, regularly improving the user experience can become problematic.

Each IT organization will need to decide for itself how best to go about building and deploying mobile applications, but the one thing that is certain is adjustments to DevOps workflows will need to be made as a lot more of these applications are continuously built, deployed and updated.

November 27, 2023
LaunchDarkly Previews Generative AI Testing Tool for Feature Management

At the AWS re:Invent 2023 conference, LaunchDarkly today previewed an ability to leverage generative artificial intelligence (AI) capabilities to create experiments for its feature management platform.

Based on the Bedrock foundational large language models (LLMs) made available via AWS, this extension to the company’s Product Experimentation platform promises to make it simpler to use historical data to create a wider range of offerings that might be made available within an application.

LaunchDarkly is making a case for a SaaS platform that can be used to centrally manage features across multiple workflows and platforms.

Robert Neal, director of engineering for LaunchDarkly, said the company is now extending its testing capabilities using a managed Bedrock service provided by AWS that will enable leveraging generative AI to define a testing hypothesis more easily. That should also make it possible to involve more stakeholders in an iterative testing process within the context of a set of well-defined guardrails, he added.

As more organizations start to deliver different tiers of digital services at varying price points, there is a clear need to be able to restrict access to those digital services. Application development teams have been employing feature management since the 1970s to isolate the development of various components into a branch that can be worked on and tested without impacting the primary build the application is based on.

Feature management enables development teams to experiment with adding new capabilities in a way that doesn’t disrupt the application. When a project is completed, that branch is then usually merged into the primary build or can be deployed as a microservice that might be invoked via an application programming interface (API) by other modules with the application or another external application.

At the core of that capability are what are known as feature flags, also known as feature toggles or feature switches, that make it possible to dynamically turn services on or off based on who is accessing them. Rather than being limited to the application development process, those feature flags are now being employed in production environments to enable the continuous delivery of multiple types of digital services to various classes of users of an application. A feature flag management platform enables an organization to keep track of all the flags being employed to keep the overall application environment consistent.

It’s still early days in terms of understanding the extent to which generative AI can be applied to DevOps workflows, but it’s clear many of the manual processes that conspire to slow down the pace at which applications are built and deployed are going to be either reduced or outright eliminated. Each DevOps team will naturally need to determine for itself to what degree DevOps workflows can be automated using AI, but the overall pace at which applications can be built and deployed is going to clearly accelerate. The challenge now will be finding ways to keep pace with a rate of application development that is about to exponentially increase.

November 27, 2023
Redgate Automates Pulling of Test Data From Production Databases

Redgate today added a Redgate Test Data Management (TDM) tool that automates the pulling of masked test data from databases running in production environments.

David Gummer, chief product officer for Redgate, said this capability eliminates manual tasks that previously inhibited testing or resulted in personally identifiable information (PPI) being inadvertently used to test applications.

Redgate TDM automatically classifies and masks data residing in SQL Server, PostgreSQL, MySQL or Oracle database before it is used to test an application. It then produces a copy or clone of that data that is a fraction the size of the original, said Gummer.

That approach creates a repeatable process for DevOps teams that can withstand a regulatory audit of application development processes, he added. In fact, with the increased focus on software supply chain issues, more auditors are specifically reviewing how data is secured during the application development process.

Designed to be invoked via a command line interface (CLI), within an integrated development environment or via a graphical interface, Redgate TDM reduces the friction that currently occurs when DevOps teams request test data from database administrators (DBAs), said Gummer. Many DBAs are either too busy to fulfill those requests or reluctant to provide access to sensitive data that would result in the organization running afoul of a compliance mandate.

Developers then often resort to using anonymous data that doesn’t actually reflect the environment in which their application will ultimately be deployed.

The amount of data that developers need to build applications is only going to increase. In the absence of any way to effectively automate the process of accessing that data, it ultimately takes longer to build and deploy stable applications. In many cases, applications are simply not tested as thoroughly as they should be before being deployed in a production environment, resulting in rollbacks that might otherwise be avoidable.

Advances in artificial intelligence (AI) are making it easier to create test scripts, but much of the testing setup process is still based on manual processes that increase the overall level of toil.

At a time when more organizations than ever are trying to increase developer productivity, automating relatively mundane tasks such as pulling test data can have a big impact. Developers want to spend as much time as possible focused on writing code. Everything else is a task that gets in the way of achieving that goal. While there is a general desire to empower developers, that doesn’t necessarily mean they want the cognitive load associated with building applications to increase. In fact, one of the reasons platform engineering has emerged as a methodology for managing DevOps workflows at scale is to reduce the current level of cognitive load developers experience today.

Of course, DevOps teams are, by their nature, ruthlessly committed to automating as many processes as possible. The challenge is bottlenecks in those processes conspire to slow down the overall rate at which applications can be built and deployed despite the best intentions of all involved.

November 16, 2023
BMC Mainframe Survey Results Show a Platform on the Rise

What if I told you that the oldest active computing platform is also one of the most innovative, seen as a platform for growth and new workloads, not just by seasoned professionals, but by a new generation of stewards, too? The 2023 BMC Mainframe Survey shows just that. In addition to an overwhelmingly positive perception and growth mindset regarding the mainframe, the survey reveals that the majority of those who are most enthusiastic about its future are relative newcomers to the platform.

Now in its 18th edition, the annual BMC Mainframe Survey gauges the opinions of more than 800 executive and technical professionals on a range of topics, from overall perception of the platform to top priorities, common practices and greatest challenges.

This year’s results show that 94% of respondents have a positive perception of the mainframe, with 62% citing it as a platform to grow and attract new workloads. This tracks the 62% who report adopting DevOps practices on the platform. It’s no surprise, then, that two of the top five priorities cited by respondents are efforts to enhance automation and modernize applications. Organizations around the world recognize that the mainframe can be a leader in innovation, powering their digital transformations.

Modern Management Approaches

This innovation goes beyond the software and services developed for the mainframe to include those used to manage the platform. It’s reported that 35% of respondents listed the implementation of cloud technologies as one of their top four priorities for the coming year, with 41% saying they are considering cloud-based storage and backup. With the growing volume of mainframe data and the emergence of new regulations and standards governing data security and recovery, organizations are turning to cloud-based data management to reduce costs and improve recovery options.

Year over year, the number of survey respondents reporting the use of AIOps hasn’t changed considerably, but those reporting its cross-platform use on both mainframe and non-mainframe systems has jumped 7% from 2021.

Organizations are recognizing the need to take advantage of the greatest business values of AIOps — cited in the survey as improved uptime and the ability to detect problems quickly, understand trends and respond proactively — as part of a holistic approach to enterprise IT operations management.

With increased cross-platform use, significant benefits and a decrease in the number of respondents reporting implementation challenges like cost, complexity and the need for specialized skillsets, BMC sees AIOps as an area ripe for significant growth in the coming year.

Investment Pays Dividends

Improving the availability, quality and efficiency of the mainframe, of course, requires investment. In our report on the results of the 2023 BMC Mainframe Survey, we compare the responses of maximizers (those planning to increase investment in the platform) with those of minimizers (those planning to decrease investment levels). The results are eye-opening.

Maximizers have invested in future-proofing their workforce in a variety of ways, and the data bears that out; 81% have ten or fewer years of experience on the platform. The new generation of stewards sees the mainframe not as a legacy platform, but one of continued growth and innovation, worthy of the investment required not just to keep it modern, but to place it at the forefront of digital transformation.

Another key difference between maximizers and minimizers relates to security, which was cited as the top overall priority of survey respondents for the fourth consecutive year. Maximizers are more likely than minimizers to have a dedicated resource to investigate security events and to have dedicated ransomware recovery capabilities in place. It should come as no surprise, then, that maximizers were 20% more likely than minimizers to report that their mainframe security controls for ransomware are extremely effective.

These results show that the differences between maximizers and minimizers go beyond outlook, producing tangible effects on outcomes. The maximizers increasing their investment in the mainframe report fewer challenges with staffing and skills shortages and a stronger stance against increasingly sophisticated cyberthreats. The minimizers decreasing their investment in the platform not only have greater difficulty in maintaining a skilled workforce, which increases the risk of falling behind competitively, but it also puts their organizations at serious risk of potentially catastrophic security breaches.

A Bright Future

In short, the results of the 2023 BMC Mainframe Survey reflect a positive outlook for the platform’s future. By investing in their workforces and the tools required to advance and protect the platform while adopting modern techniques and practices, organizations can reduce risk and promote innovation that delights their customers.

November 16, 2023
GitHub Aims to Expand Copilot Scope and Reach in 2024

GitHub is gearing up to launch Copilot Workspace next year, a platform that will leverage generative artificial intelligence (AI) to automatically propose a plan for building an application based on natural language descriptions typed into the GitHub Issues project management software.

The platform, revealed at the GitHub Universe 2023 conference, Copilot Workspace will generate editable documents via a single click that can be used to create code that developers can then visually inspect, edit and validate. Any errors discovered by application developers or the Copilot Workspace platform can also be automatically fixed.

In addition, summaries of the project can automatically be created and shared across an application development team.

GitHub CEO Thomas Dohnke told conference attendees this “revolutionary” approach will enable developers to employ AI as a “second brain.”

In the meantime, GitHub is making an enterprise edition of Copilot available that can be trained using code connected to a private repository to ensure intellectual property is protected. GitHub also moving to integrate GitHub Copilot with third-party developer tools, online services and knowledge outside GitHub by collaborating with, for example, Datastax, LaunchDarkly, Postman, Hashicorp and Datadog.

GitHub is moving to make the generative AI capabilities it provides accessible beyond text editors. Copilot Chat, starting next month, can be accessed via a mobile application to foster collaboration by explaining concepts, suggesting code based on your open files and windows, detecting security vulnerabilities and finding and fixing code errors.

Copilot Chat, based on Chat GPT 4, can also be accessible across the GitHub website in addition to integrated development environments (IDEs) such as JetBrains and via a command line interface (CLI).

Generative AI is already having a massive impact on the rate at which applications are developed, but that code still needs to be reviewed. Chat GPT is based on a general-purpose large language model (LLM) that is trained by pulling in code of varying quality from all across the web. As a result, code generated by the platform might contain vulnerabilities or be inefficient. In many cases, professional developers still prefer to write their own code.

Of course, not every programming task requires the same level of coding expertise. In many instances, ChatGPT will generate, for example, a script that can be reused with confidence across a DevOps workflow. There is no shortage of mediocre developers who are now writing better code thanks to tools such as GitHub Copilot, and soon, domain-specific LLMs will make it possible to consistently write better code based on validated examples of code.

The one thing that is certain is the volume of code written by machines is only going to increase. The challenge will be managing all the DevOps pipelines that will be needed to move increased volumes of code into a production environment. There is no doubt that AI will be applied to the management of DevOps pipelines, but for the moment, at least, the pace at which AI is being applied to writing code is already exceeding the ability of DevOps teams to manage it.

November 15, 2023
Canonical Automates Provisioning of Cloud Environments Anywhere

Canonical today made generally available an open source framework, dubbed MicroCloud, that leverages Linux containers and the Snap software packaging and deployment tools the company developed to automatically orchestrate the provisioning of cloud computing environments using a single command.

MicroCloud automatically creates a functional cloud computing environment in minutes that can be deployed anywhere, including edge computing environments, using policies defined by an internal IT team to ensure the appropriate guardrails are in place. All components can also be updated over the air in a way that preserves data, and if any errors occur, rollbacks are automated.

Miona Aleksic, product manager of MicroCloud, said the platform enables organizations to democratize DevOps workflows in a way that becomes accessible to the average IT administrator.

In effect, Canonical is making cloud computing more accessible in a way that is reminiscent of its creation of the Ubuntu distribution of Linux that is easier to deploy and install, she added.

As IT environments become more distributed, programmatically spinning up infrastructure resources has become more challenging. MicroCloud enables IT teams to provision resources more easily at the point where data is being created, analyzed and consumed, noted Aleksic.

It should also improve cybersecurity because many of the cybersecurity issues that organizations encounter today can be traced back to infrastructure misconfigurations, she added.

It’s not clear how automated the provisioning of IT environments is about to become, but the demand for applications today far exceeds the ability of many DevOps teams to keep pace. Much of the toil that leads to burnout among DevOps teams stems from the manual tasks that persist within workflows. In theory, the more low-level tasks—that don’t add much in the way of differentiated value to the business—can be automated, the more time there should be to address more complex issues.

That’s critical at a time when many organizations still struggle to find and retain DevOps engineers. In addition, the simpler it becomes to automate the provisioning of infrastructure, the more accessible DevOps best practices become for small-to-medium sized businesses (SMBs) that historically couldn’t afford to hire software engineers.

One way or another, DevOps workflows will need to become more automated. There is naturally a lot of focus on artificial intelligence (AI), but while that approach holds a lot of promise, there are clearly advances to be made using a range of existing automation frameworks. The challenge organizations have encountered is that writing the playbooks required to automate processes requires a level of expertise they generally lack.

Platforms such as MicroCloud present IT teams with an alternative approach that should reduce the cognitive load developers encounter whenever they want to provision IT infrastructure. Arguably, the one reason developers provision infrastructure themselves is that previously it simply took too long for IT teams to do it on their behalf.

It may be too early to determine whether or not tools such as Terraform are required to provision infrastructure, but it’s clear that most developers would rather spend their time writing code instead of managing infrastructure.

November 14, 2023
Leveraging Crowdtesting in DevOps to Enhance Quality Assurance (QA)
There’s no doubt that quality assurance (QA) within the DevOps culture of continuous integration and deployment helps to ensure the ongoing quality of software releases. Especially as QA experts collaborate with development and operations teams to ensure that the software meets quality standards, is free of defects and aligns with customer expectations in an efficient and timely manner.

But, not surprisingly, there are still challenges for QA teams within DevOps. One of the most significant is the need to “shift left” and be involved in testing and quality activities earlier in the development cycle, which requires adapting to faster development cycles and collaborating closely with developers to integrate testing and quality practices from the start.

This places additional stress on QA experts who must react faster while being increasingly tasked with building and maintaining robust automated testing frameworks. When timelines matter, this can become a considerable issue.

Additionally, QA professionals must also work collaboratively with developers, operations teams and other stakeholders. If cultures are opposed, this can severely impact development cycles.

Lastly, with frequent code deployments and configuration changes, maintaining consistent and reliable test environments becomes crucial.

But amid these challenges, there are many opportunities.

QA experts can contribute to early and continuous testing, collaborate with cross-functional teams, leverage automation and tooling, engage in continuous learning and take ownership of end-to-end quality. QA experts play a crucial role in ensuring high-quality software delivery in the DevOps environment.

Crowdtesting Within DevOps

Crowdtesting can play a significant role in DevOps throughout the entire software development life cycle (SDLC). Here are just a few benefits:
1. Faster Feedback: Crowdtesting brings diverse testers with various devices and configurations, allowing for quick issue identification and resolution in agile development.
2. Scalability and Flexibility: Crowdtesting easily scales up or down based on project needs, accommodating frequent updates and releases and ensuring testing coverage even during peak periods.
3. Real-world Environments: Crowdtesting provides insights into software performance under diverse conditions, including network speeds, hardware configurations and geographic locations, improving quality and user experience.
4. CI/CD Integration: Crowdtesting seamlessly integrates into CI/CD pipelines, enabling continuous testing of each iteration, catching issues early and reducing regression risks.
5. User Feedback: Crowdtesting gathers feedback from real users, offering valuable insights on usability, functionality and customer experience, aiding decision-making and feature prioritization.
6. Localization and Internationalization: Crowdtesting helps test localized versions and internationalization support, incorporating language translations, cultural nuances, and regional requirements, ensuring a global user base and enhanced market reach.
Automation and DevOps

Crowdtesting can also complement automation.

Within DevOps, automation plays a crucial role in continuous integration, delivery, and also testing – especially for unit testing, integration testing, functional testing, and performance testing – but there are times when manual testing, including crowdtesting, remains relevant and beneficial.

Do you want a realistic way to assess the usability and user experience aspects of your software? Human crowdtesters can provide subjective feedback, identify user experience issues, and offer suggestions for improvement – all within real-world scenarios that automated tests may miss.

Crowdtesters also possess the creativity and intuition to perform exploratory testing, where they actively explore the software to uncover unforeseen bugs, usability issues, or edge cases.

The same goes for user acceptance testing, where a specific target audience evaluates the software against their specific needs and requirements. These types of testing require human intelligence and critical thinking skills that automation cannot easily replicate.

This is especially true when it comes to edge cases, unusual scenarios and non-standard user interactions, which often require creative thinking. Manual crowdtesting can uncover potential issues that automated tests overlook.

But ultimately, it is the ability to test in diverse real-world environments, configurations and geographical locations where crowdtesting makes the biggest impact.

By leveraging both approaches, you can achieve a robust testing process that ensures software quality, usability and a positive user experience.
November 14, 2023
Elevating Quality Engineers in an AI-Driven Landscape

Artificial intelligence (AI) is quickly transforming software testing and quality assurance (QA) by powering game-changing capabilities like autonomous testing, intelligent test case generation and real-time risk analysis. As a result, it is creating new opportunities for AI-savvy test engineers. But that doesn’t mean that you can set it and forget it.

AI helps us work smarter by eliminating repetitive tasks. Think of it like a supercharged intern who can rapidly analyze huge datasets, suggest myriad test ideas and point out risks — all at incredible speed and scale. But also, like most interns, AI needs some guidance. With the addition of thoughtful human oversight and control, AI tools can ultimately enhance the work of test engineers, and even alleviate stress by automating mundane work. In the end, however, human test engineers will remain the key decision-makers.

Buckle Up: The Landscape is About to Change

AI enables QA professionals to focus more time and energy on strategic, high-value testing activities by automatically handling rote script maintenance, data management and other repetitive tasks that consume excessive time and effort. It can eliminate the grunt work so test engineers can focus on more rewarding testing aspects of their jobs. AI also reduces over-reliance on specialized technical expertise by shifting the focus back to foundational testing skills like business domain knowledge, advanced risk analysis and cross-team collaboration. Ultimately, a test engineer’s true value is a combination of their experience and their domain knowledge, not just their tech skills. And AI can help bring these to the forefront.

As AI helps developers produce more code faster, it will create more work for quality assurance testers. Using AI to automate their test plans, test engineers will be able to manage greater volumes of throughput without feeling overwhelmed by it. To be sure, this will all be a boon to the software industry as a whole. So, buckle up and prepare for more — not less — work.

Broad adoption of AI will organically transform QA roles into more advisory-focused, strategic positions focused on optimizing end-to-end testing processes versus hands-on test execution. Test engineers will transition from being “tactical doers” to a role akin to a strategic coach who guides the technology for maximal impact.

AI-enabled autonomous testing tools that automatically generate test cases, execute scripts, manage test data and identify risks will become standard components of future QA stacks, allowing test engineers to achieve much higher test coverage rather than simply automating happy path testing. But this can only happen if those in control ask the right questions, establish sound guardrails and apply the unique skills only a human can provide.

The Importance of Human Judgment — Don’t Relinquish Control

The key to the successful integration of AI into your testing program is to remain skeptical. It is unwise to blindly trust in the opaque limitations of an AI tool. It also helps to know how the technology works under the hood. In fact, irresponsible use of AI can be dangerous, especially if we don’t understand its limitations.

Testers need consistent human judgment and involvement. Human skills like customer empathy, communication and creative problem-solving remain critical, and AI will never replicate these distinctly human capabilities. By emphasizing their focus on users, systems and applications, creative testers will thrive in the presence of AI automation.

It is not only software testers who need to embrace change but developers of testing tools, as well. It is important that whoever creates the platforms for testing provides transparency into how their tools work. AI tool developers should avoid “black box” development and provide visibility into their models. When a test engineer understands how an AI tool works under the hood — and how it is limited — they will be in a better position to trust and verify the tool’s output. This open collaboration between tool developers and test engineers can maximize benefits and minimize risks in our industry.

Preparing for an AI-Driven Future

To best prepare for the adoption of AI-powered QA tasks, enterprises must invest in proper change management, provide extensive AI training and promote open communication. For their part, test engineers can benefit from engaging with each other to share their AI experiences — whether positive or negative.

In the short term, the software test industry needs leaders to take a measured approach by piloting AI tools on targeted use cases, versus attempting to overhaul all of testing overnight. Start small with low-risk AI projects, then scale what works.

While AI promises to change the testing status quo forever, human insight will remain the indispensable ingredient for responsible, successful software releases. Creative testers who master AI will drive the greatest innovations. Those who embrace this exciting time in our industry are sure to come out ahead of the pack.

November 14, 2023
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

CareerBuilder.com

Walgreens
Deerfield, Illinois
Senior DevOps Engineer
$120,000 to $170,000

Indeed.com

American Red Cross
Raleigh, North Carolina
DevOps and Test Engineer
$120,000 to $130,000

LinkedIn

U.S. Bitcoin Corp.
Miami, Florida
Senior Software Engineer
$120,000 to $130,000

SimplyHired.com

Utah Health Information Network (UHIN)
Remote
Principal DevOps Engineer
$117,000 to $170,000

Dice.com

Garnet River
Buffalo, New York
Cloud DevOps Manager
$90,000 to $145,000

November 13, 2023
Elastic Previews Unified Query Language for Search Platform

Elastic this week previewed a standard query language that can be used across its portfolio to streamline investigations into IT and cybersecurity events.

Gagan Singh, vice president of product marking for Elastic, said Elasticsearch Query Language (ES|QL) makes it simpler, via a single query, to aggregate data from multiple sources using a piped query syntax that makes it simpler to iteratively interrogate data.

Scheduled to become generally available in 2024, ES|QL replaces the multiple domain-specific languages the company has been providing for each version of its search engine. Instead of having to move data into a central repository, Elastic Seach makes it possible to query data wherever it resides in an organization, noted Singh.

That approach enables, for example, a site reliability engineer (SRE) to discover the root cause of an issue through a series of queries that can be launched from a familiar search interface, he said. It will also foster increased collaboration across IT teams that need to visualize query results, added Singh.

In general, IT teams are struggling with an exponential increase in the amount of data they need to analyze to discover the root chaise of an issue, noted Singh. The Elastic search engine provides a means to analyze that data at the level of scale that is now required, he added.

The preview of ES|QL comes on the heels of a technical preview of a generative artificial intelligence (AI) tool for observability the company has made available. The Elastic AI Assistant for Observability makes use of large language models (LLMs) and the Elasticsearch Relevance Engine (ESRE) to explain application errors, log messages, alerts and recommendations for improving code in plain language. DevOps teams can visualize and interactively chat with telemetry data and then invoke runbooks to remediate issues.

Elastic has also generally made available a Universal Profiling tool to enable DevOps teams to identify bottlenecks that adversely impact code performance.

There is little doubt that fundamental changes to how DevOps and cybersecurity teams interact with telemetry data are on the way. The challenge now will be determining when to rely on a generative AI tool versus diving deeper into that data by launching a series of queries. At the very least, AI should provide DevOps and cybersecurity professionals with a better sense of where to start an investigation.

Ultimately, the goal should be to reduce the level of DevOps and cybersecurity effort that is currently required to succeed. In theory, as it becomes simpler to investigate issues, the number of organizations capable of embracing DevOps best practices should increase as well. That’s crucial because IT environments are only becoming more complex with each passing day. The number of dependencies that exist between services has become too numerous for IT teams to track manually. More challenging still, those services are dynamically being updated, so what was known today about them might no longer be relevant tomorrow.

Hopefully, the amount of time and toil required to address issues will significantly decline in the months ahead as the next generation of tools becomes available. The challenge now is hanging on long enough for them to be battle-tested.

November 10, 2023