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Tricentis Preps Wave of Additional AI Testing Capabilities

Tricentis is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI agent, dubbed Aida, that explores web and Windows desktop applications to surface defects, weaknesses and other potential gaps without requiring DevSecOps teams to create scripts.

Additionally, Tricentis is developing a Release Risk Intelligence capability that surfaces release-scoped coverage gaps, prioritizes risks by severity, and launches AI tasks to resolve issues that are informed by context provided by the automated Tricentis testing platform.

Finally, Tricentis is also readying AgentScore, a tool that enables organizations to evaluate AI agents by observing how agents behave in real-world workflows, recommending what should be measured, and generating composite quality scores that include review, block, or ship recommendations.

David Colwell, vice president of AI and machine learning for Tricentis, said the overall goal of the Tricentis Transform initiative is to ultimately make AI application testing a more integrated element of building and deploying any application rather than being an entirely separate function. The end result should be higher quality applications that are inherently more secure, he added.

The launch of the Tricentis Transform initiative comes on the heels of the company’s acquisition of Tabnine, which developed a knowledge graph that AI agents can invoke to discover the relationships between various application components. Armed with insights that are uniquely provided by a testing platform, AI agents are provided with the context needed to automate tasks much more reliably, noted Colwell.

It’s still early days so far as embedding AI agents into DevOps workflows is concerned, but as more code is generated using AI tools, there is a corresponding increase in the amount of pressure on existing pipelines. The first place that pressure is manifesting itself is in the need to review and test code at machine speed. In the absence of that capability, more AI-generated code is being deployed in production environments with predictable results. The number of incidents in production environments that have been traced back to AI code has already begun to exponentially increase, noted Colwell.

Exactly how long it might take to re-engineer DevOps workflows in the age of AI remains to be seen, but it’s apparent that testing will become more integral. The difference is that much of that testing is likely to be conducted by AI agents that are reviewing and analyzing code created by other AI agents. The challenge, in the meantime, is a coverage gap that is widening as the volume of code being generated overwhelms the ability of DevOps teams to test it.

Hopefully, there will come a day when the distinction between creating and testing code becomes so blurry as to be all but meaningless. That doesn’t mean the need for specialists to test applications will completely disappear, but it does mean that any issues that do require their expertise will be worth the time and effort for them, rather than an AI agent, to personally resolve.

August 26, 2026
Tricentis Acquires Tabnine to Gain Knowledge Graph for AI Testing Agents

Tricentis today revealed it is acquiring Tabnine to gain access to a knowledge graph that will be used to provide context to AI agents that have been trained to automate a range of testing tasks.

Once the acquisition is complete, Tricentis plans to integrate the knowledge graph developed by Tabnine, dubbed the Enterprise Context Engine, into the company’s Agentic Quality Engineering Platform. Coupled with a vector model, that knowledge graph makes it possible to extract entities, relationships, dependencies, and architectural patterns from repositories, documentation, tickets, application programming interfaces (APIs) and infrastructure metadata in a way that is much easier for AI agents to consume and understand.

The end result will be a platform that continuously ingests code, documentation, tickets, and APIs, providing organizational intelligence in real time and enabling multiple AI agents to share memory and context to both verify outputs and better understand the potential downstream impact of an action.

David Colwell, vice president of AI and machine learning for Tricentis, said that, just as importantly, the capability makes it possible for AI agents to more efficiently test code in a way that serves to reduce the total number of tokens that might otherwise be required.

It’s still early days when it comes to incorporating AI agents into DevOps workflows, but the one thing that is already clear is there is now a greater need to rely on an independent set of AI agents to verify the output of AI coding agents. In some cases, the AI agents performing testing tasks may rely on the same AI model as the coding agents, but as a general rule, the best practice would be to rely on a separate AI model, said Colwell.

Regardless of approach, unless AI agents are integrated into testing workflows, there will be no way for DevOps teams to keep up with the pace at which code is now being generated. The end result would then be more untested code than ever making its way into production environments, which ultimately only serves to increase the total number of incidents that DevOps teams will need to later triage and remediate, noted Colwell.

A recent Tricentis survey finds 60% of organizations already regularly ship untested code into production environments. With more than two-thirds (68%) of organizations having to some degree integrated AI into software delivery workflows, it has only become more probable that untested code is finding its way into production environments. The challenge and the opportunity now is to find a way to reduce that percentage without slowing down the rate at which code is now being created.

There is, of course, a world of difference between shipping code faster and actually delivering more value to the business. The assumption has always been that more code would by definition enable organizations to provide more value to their end customers by delivering more features and capabilities faster. Unfortunately, every time there is an incident it also serves to reduce the return on investment (ROI) from accelerating software delivery in the first place, an issue that in the age of AI has become more concerning as the volume of code being created and deployed exceeds the ability of most DevOps teams to effectively manage.

July 30, 2026
Survey Surfaces Pervasive Adoption of AI Across SDLC

A global survey of 2,501 IT and DevOps professionals at organizations with more than 150 employees published today finds more than two-thirds (68%) work for organizations that have implemented artificial intelligence (AI) across some or all their software delivery workflows.

Conducted by Tricentis, a provider of a platform for testing software, the survey identifies enhanced quality and risk detection (37%), enhanced accuracy and consistency (36%) and improved test automation coverage (32%) as the top benefits of integrating AI into those workflows

Overall, 53% manage between six and ten AI or automation tools across their software development lifecycle (SDLC).

However, the survey also finds that 60% admit their application developers also regularly ship untested code into production environments.

David Colwell, vice president of AI and machine learning at Tricentis, said that as more AI-generated code is created, the volume of code that has not been tested is increasing, which in the long term is going to have a negative impact on the overall quality of the applications being deployed.

That issue may become further exacerbated with the rise of AI agents. Only 35% said they are fully prepared to govern AI agents, with security concerns (27%) and regulatory and compliance requirements (22%) being the two biggest blockers to adoption.

In general, the top business impacts of the poor quality software cited by survey respondents are security breaches and compliance failures (30%), technical debt and rework costs (28%) and loss of customer or partner trust (25%).

The main operation challenges when it comes to implementing continuous best practices for software quality are tool sprawl/operational complexity (33%), code volume scaling faster than teams can manage (28%) and gaps in AI-related skills and expertise (27%).

A full 20% of respondents also noted their organization reported annual losses of up to $5 million that were attributed to poor software quality.

Not surprisingly, the top two reasons code is not tested are pressure from senior leadership to accelerate software delivery (32%), followed closely by there being too much code to test (30%).

Unfortunately, too many application developers have too much faith in the quality of the code being generated by AI coding tools as there continues to be more emphasis on speed than quality, said Colwell. As the volume of code being generated is only going to increase in the age of AI, it’s only a matter of time before more time and attention is placed on the quality of that code, he added. Eventually, the cost of poor quality software will become too big to bear as more issues arise in production environments, noted Colwell.

Ultimately, each DevOps team will need to strike the right balance between speed and quality. The one thing to remember in the age of AI is that the business benefits of meeting software delivery deadlines are, as always, negated when the quality of the software delivered winds up having a negative impact on the business that far exceeds any perceived cost created by an application that wasn’t delivered exactly on time.

June 3, 2026
Tricentis Looks to Further Automate Application Testing

Tricentis has extended a cloud-based testing service to make it easier to ensure that test cases are designed to continuously validate business workflows against mission-critical business objectives, not just technical functionalities.

In addition, the latest version of Tricentis Tosca also makes it simpler to make use of reusable test step blocks using a set of elastic execution agents that enable DevOps teams to consume infrastructure resources more efficiently at a time when there is greater sensitivity to the total cost of IT.

At the same time, Tricentis has revealed it plans to add a set of generative artificial intelligence (AI) agents to further automate testing tasks. Previously, Tricentis has made available a set of AI co-pilot tools across its test automation platforms, but with the rise of agentic AI the goal will soon be finding a way to offload tasks to a series of AI agents that are managed via the Tricentis platform.

Adnan Ćosić, a senior product marketing manager for Tricentis, said the overall goal is to make it simpler for DevOps teams to cost effectively create the right test for the right use case using a combination of agents and Business Flow Designer, a graphical tool that provides no-code approach to validating business workflows and their data dependencies. DevOps teams can also employ a set of dashboards that have been added to surface real-time insights into metric and actual test execution progress.

Tricentis Tosca already leverages machine learning algorithms to automate the management and provisioning of test data to reduce the level of manual effort that would otherwise be required. As the volume of code being created continues to exponentially increase in the age of generative AI, the need to find ways to automate the testing of applications will become more acute, noted Ćosić. In fact, some organizations might soon find themselves building and deploying more applications in the months ahead than they have in the last several years combined.

It’s not clear to what degree organizations are relying on dedicated application testing teams as more responsibility for the quality of the applications being developed has in recent years shifted further left toward application developers. However, in theory as application developers discover and remediate more issues on their own, application testing teams should gain the time needed to conduct more rigorous tests before applications are deployed in production environments.

Regardless of approach, however, it’s clear that as application testing becomes more automated it should become feasible to run more tests. One of the issues that many organizations still contend with is the simple fact that as application delivery deadlines approach, one of the ways that lost ground is made up is by reducing the number of tests that might otherwise be conducted.

Hopefully, as AI is more broadly applied to testing, the overall quality of the applications being deployed will continue to steadily improve. In the meantime, each DevOps team should be reviewing which testing tasks might soon be offloaded to, for example, an AI agent. After all, many testing tasks are tedious, which may account for why they are often not as thoroughly conducted as everyone might otherwise like.

May 6, 2025
Breaking Down Testing Barriers: How Observability is Bridging Dev and Ops

The traditional divide between development and operations rapidly dissolves as modern observability practices transform software testing. According to Bryan Cole, Director of Customer Engineering at Tricentis, this evolution is crucial for delivering business value.

“Ops teams are paid to keep the lights on and deliver business value,” Cole explains. “What Dynatrace is doing, moving into the dev space, is providing context to the ops teams on what exactly is happening, and more relevantly, how things are testing out.”

This context is critical. “Nothing’s worse for an IT ops team than to be given a software build that you know nothing about to deploy Friday night,” Cole notes. “Hope is a terrible strategy.”

The integration between Tricentis NeoLoad and Dynatrace exemplifies how observability is transforming testing practices. Cole highlights how Davis AI can parse through billions of data points across complex software systems, providing focused guidance to operations teams. “That insight provides focus and guidance to the ops teams and helps them manage their time, because the ops are getting so big and complex, you can’t understand everything, let alone look at everything.”

The results are significant. Cole shares examples of customers seeing dramatic improvements: “A large retail site saw an 80-90% reduction in time to analyze performance test results. A healthcare provider experienced a 90% reduction in problem identification time and a 95% reduction in the mean time to resolution.”

Preventive operations are becoming increasingly important. “If you don’t know the symptoms your application is going to exhibit when it starts to fail, then you have a hard time understanding what should trigger you to take that preventive action,” Cole explains.

The challenge lies in changing perceptions about continuous testing. “The message of implementing quality gates and additional checks doesn’t resonate well with delivery faster,” Cole notes. “But many people are guilty looking at that narrow slice of time of just the delivery of the software in a production environment, not the consequences of it arriving in production and not working properly.”

The future of testing lies in accessibility and integration. Cole emphasizes making data accessible across platforms: “We want to make your data accessible to you, to use the way you want, in the platform where you’re comfortable and familiar.”

Mitch Ashley, VP and Practice Lead, DevOps and Application Development, The Futurum Group, believes, “Software is no longer static. It is constantly changing, closer to a fluid state than a solid. We need the production telemetry pipeline to flow upstream and down between developers, QA and Ops. No one vendor can do it alone. Partnerships such as those between Tricentis NeoLoad and Dynatrace enable customers to benefit from two-way telemetry easily.”

This integration enables what Cole calls “the continuous quality practice,” where “the volume of changes goes up, but the size of the changes goes down.” Combined with AI-powered observability, this targeted approach transforms how teams identify and resolve issues before they impact production.

February 6, 2025
Global Survey Finds Testing as the Most Valuable AI Investment Across the Software Development Lifecycle
Tricentis report reveals AI-augmented DevOps tools will save teams over an estimated 40 hours per month—equivalent to an entire workweek

AUSTIN, TEXAS – August 6th, 2024 – Tricentis, a global leader in continuous testing and quality engineering, today released the findings of its most recent AI report, “AI-augmented DevOps: Trends Shaping the Future.” This year’s research aims to understand to what extent the anticipated benefits of AI in DevOps have been realized today, and how a lack of trust, skills, or other challenges could affect its adoption.

When asked to evaluate the most impactful areas for AI investments across the delivery cycle – such as planning, coding, deploying and releasing – DevOps practitioners ranked testing as the most valuable (60%). This result was foreshadowed in the Tricentis’ 2022 study, which found that testing is where organizations expected the greatest value from AI-augmented DevOps, with nearly 70% of respondents having rated the potential of AI-augmented testing as extremely or very valuable.

The research finds that DevOps teams are realizing the benefits of AI, with mature DevOps teams who have adopted AI significantly more likely (30%) to rate their teams as either extremely or very effective. The biggest challenges DevOps teams are using AI to address are developer team efficiency (60%), reducing the skills gap (54%), cost reduction (47%), and software quality (42%). In fact, almost one third (32%) of respondents estimate AI-augmented DevOps tools will save teams over 40 hours per month—equivalent to an entire workweek.

The 2024 results show that teams use AI to augment a wide range of testing tasks, including test planning/deciding what to test (47.5%), test case generation (44%), and analyzing test results (32%). Additionally, nearly half (42%) of respondents expect AI to perform a risk analysis of code changes, helping QA teams focus on code areas with the greatest risk of errors to quality.

The report surveys 500+ DevOps practitioners, managers, and executives from small, mid-size, and enterprise organizations across the globe and in several industries, including financial services, healthcare, and manufacturing.

Other 2024 findings reveal:
- Regulation is expected to help build trust, but others worry it will stifle innovation and outcomes. Nearly two thirds (63%) of those surveyed view increased regulation as a way to build confidence in AI across their organization, while a smaller—but not insignificant—number of DevOps practitioners (16%) feel increased regulation will hinder or stifle the potential impact of AI in organizations.
- Humans remain vital to ensure software quality, but there’s a shortfall in AI skills. Data shows that humans are still very much “in the loop,” with over two thirds (71%) of respondents checking outputs at least half of the time, and almost one in five (19%) claiming to check AI outputs all of the time. However, a lack of AI skills (28%) is seen as the greatest hurdle to AI adoption in DevOps.
- Generative AI (Gen AI) and AI copilots are key drivers of AI adoption: GenAI (45%) is now the most widely adopted type of AI used by DevOps practitioners. In particular, AI copilots are also on the rise, with use cases in planning, code development, and software testing.
“AI is an exciting technology and growing at a pace unlike anything we’ve seen in our industry,” said Mav Turner, Chief Product and Strategy Officer, Tricentis. “As AI technology is further developed, however, training software development and quality engineering teams with the necessary skills to effectively work with AI will be absolutely critical.”

“DevOps teams looking to get started with AI should look no further than their testing processes. AI in testing helps to detect, auto-heal and predict defects during development, as well as identify which tests need to be run based on high risk. When coupled with low-code/no-code technology, this means that, regardless of a team’s technical expertise, AI can significantly contribute to overall software quality. As DevOps teams continue to mature, testing will be pivotal to realizing their investment in AI-augmented DevOps tools and practices.”

To view the full findings of the report, download the complete “AI-augmented DevOps: Trends Shaping the Future” report here and register for the upcoming webinar on Thursday, August 29th here.

Additional resources:
- Tricentis 2022 Report: AI-Augmented DevOps: The Next Frontier
- Press release: Tricentis Survey Reveals Majority of Organizations Recognize Value in AI-Augmented DevOps
- Tricentis Copilot
- Tricentis Tosca Copilot
- Tricentis Testim Copilot
- Tricentis AI-powered quality engineering solutions
- Tricentis AI Trust Center
Survey Methodology

The findings are based on a global study conducted by Techstrong Research on AI augmentation in DevOps environments. The survey was fielded from April 19 to June 30, 2024, and yielded responses from 500+ DevOps practitioners, managers, and executives in the Americas, Europe and the Middle East, and the Asia-Pacific from small, mid-size, and enterprise organizations.

About Tricentis

Tricentis is a global leader in continuous testing and quality engineering. The Tricentis AI-based, continuous testing portfolio of products provide a new and fundamentally different way to perform software testing. An approach that’s totally automated, fully codeless, and intelligently driven by AI. It addresses both agile development and complex enterprise apps, enabling enterprises to accelerate their digital transformation by dramatically increasing software release speed, reducing costs, and improving software quality. Widely credited for reinventing software testing for DevOps, cloud, and enterprise applications, Tricentis has been recognized as a leader by all major industry analysts, including Forrester, Gartner, and IDC. Tricentis has more than 3,000 customers, including the largest brands in the world, such as McKesson, Allianz, Telstra, Dolby, and Vodafone. To learn more, visit https://www.tricentis.com.
August 6, 2024
Tricentis Adds Additional AI Copilots to Test Automation Platform

Tricentis this week made generally available a copilot add-on for its Tricentis Tosca test automation platform. The add-on simplifies finding, understanding, and optimizing tests via a chat interface.

Mav Turner, chief product and strategy officer for Tricentis, said the company is adding copilots to each of its product offerings using large language models (LLMs) developed by Open AI. Previously, Tricentis launched Tricentis Testim Copilot; the company plans to add a Tricentis Copilot for Tricentis qTest shortly .

Over time, those AI assistants will be able to collaborate on tasks with each other and eventually with AI assistants provided by other providers of other platforms, including SAP, Turner added.

In the meantime, DevOps teams can use Tosca Copilot to find unused test cases, duplicate tests, unlinked assets, specific executions, and tests linked to application elements. Turner noted that the summarizations enabled by Tosca Copilot should make it simpler for DevOps teams to onboard additional testers.

DevOps teams can also make changes or modifications to any combination of those tests via a generative AI prompt rather than having to directly master the existing Tosca Query Language. That capability should also significantly reduce the time and effort currently required to troubleshoot test defects, said Turner.

Overall, Tricentis reports that time spent on complex testing activities can be cut in half by, for example, reducing the number of repetitive testing tasks previously required. In fact, Tricentis is already seeing a 16% to 43% reduction in test failure rates with Tricentis AI tools and up to a 50% increase in test case generation.

The goal is to not necessarily to increase the number of tests being run as much as it is to make sure the right tests are being conducted at the right time to improve application quality, said Turner.

In will, however, also become more feasible for any member of a DevOps team — including application developers — to iteratively generate a test. That’s better than having to wait on a dedicated testing team to create a test. Generative AI should also make it easier for DevOps teams to reuse tests, run tests faster, generate fewer errors, reduce costs and increase overall productivity.

It’s not clear yet to what degree generative AI will democratize application testing, but as tests become easier to create there should be more time to run a wider range of tests that might, for example, address cybersecurity issues. Most of the tests that are run today typically surface common programming mistakes and there is still a tendency to skip testing any time an application development projects starts to fall behind schedule.

However, as testing becomes faster in the age of AI there should be more time to run a wider range of tests. The challenge is finding the best way to orchestrate those tests across a portfolio of applications that is only going to become larger as AI tools make it simpler for developers to write code faster.

June 5, 2024
Survey Sees AI Playing Larger Role in Test Automation

A survey of 1,028 senior IT professionals and application developers in the United Kingdom, U.S., Germany and Singapore finds nearly half (47%) still use manual testing for mobile applications. More than a third (38%) noted that their organization could save between half and three-quarters (51-75%) of funding allocated to testing per year by fully automating mobile application testing.

Conducted by the market research firm Censuswide on behalf of Tricentis, a provider of a test automation platform, the survey also found that just under half of respondents (49%) reported their organization is already applying artificial intelligence (AI) to application testing. Another 21% plan to apply AI in the next six months. Almost three-quarters (74%) said their organization’s general sentiment toward AI in mobile software development testing is positive.

Respondents also expected AI to improve mobile application quality (37%), increase productivity (36%) enhance end-user experience (34%) and to reduce the number of vulnerabilities and/or bugs (32%) in their applications.

Mav Turner, chief product and strategy officer at Tricentis, said that rather than continuing to rely on expensive testing services, more organizations are looking to reduce costs by automating a wider range of testing workflows within the context of a DevOps workflow. In fact, the survey finds just under a third of respondents (32%) expect their organizations will invest more than $1 million to further automate testing.

As modernization of testing processes continues, the return on investment in mobile application development will only continue to improve, noted Turner.

The survey identifies the primary benefits of investing in test automation, which are increased productivity (32%), increased revenue (31%), increased user retention (28%) and increased compatibility across devices (28%).

The primary obstacles organizations encounter as they strive to achieve that goal are competing priorities (28%), lack of technology and talent (25%), time (25%) and cost (24%), the survey finds.

A full 87% said the quality of mobile applications is critical to their business, with 90% noting that poor mobile application quality costs their business as much as $2.49 million in lost revenue per year. However, just over a quarter (27%) said they believe their organization’s current mobile application development and testing strategy exceeds expectations.

As part of efforts to automate testing, it’s not clear just how far responsibility for testing is shifting left toward developers, but there is no doubt many more are being exposed to the results earlier in the software development life cycle. The goal is to provide continuous testing capabilities without overly disrupting code writing. It’s also becoming simpler for developers to leverage generative AI tools to provide summaries of tests along with recommendations to improve code. The end result should be higher-quality applications that require less troubleshooting once deployed in a production environment.

In the meantime, as organizations continue to become more dependent on mobile applications to drive a wide range of digital transformation initiatives, the cost of testing will continue to pale in comparison to the potential amount of lost revenue that might occur when end users either don’t invoke a specific feature or capability or, more commonly, simply abandon the application altogether.

March 29, 2024
Tricentis Launches Mobile Application Testing Grid

Tricentis today added a Virtual Mobile Grid service to its portfolio to make it simpler to test mobile applications at scale.

The service is based on the platform Tricentis gained via its acquisition of Waldo and is now integrated within the Tricentis Mobile application testing framework.

Tricentis CTO Mav Turner said the Virtual Mobile Grid extends an existing Tricentis Device Cloud service for accessing mobile devices to include the ability to author, execute and analyze tests that can be run across thousands of real and virtual devices.

In effect, Tricentis Mobile brings together Tricentis Testim Mobile, Tricentis Tosca Mobile and the Tricentis Device Cloud into a single platform, he noted.

DevOps teams can now test end-to-end at every layer of a mobile application using a platform that leverages neural network technology to test user interfaces. They can also build and run no-code tests to identify and resolve critical user interface and performance issues through real-time tracking of more than 130 key performance indicators (KPIs).

Organizations building mobile applications that must be deployed at scale and regularly updated are integrating testing platforms such as Tricentis within their continuous integration/continuous deployment (CI/CD) platforms. Many of those efforts are tied to digital business transformation initiatives that rely on mobile computing platforms to access a wide range of backend services.

There is a lot of debate over how far left application testing should be shifted. In theory, the more developers test applications themselves, the fewer issues there should be once an application is deployed in a production environment. However, organizations need to be wary of increasing the cognitive load on developers, especially to the point where they spend significantly less time writing code, noted Turner. There will still be a need for dedicated testing teams to conduct tests across multiple stages of the application development process to ensure quality, he added.

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

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

Theoretically, as more artificial intelligence (AI) is applied to the testing process, the number of reasons tests are not run as frequently as they should be will steadily decline. Today, it is still too difficult for many developers to author tests, so they naturally don’t conduct as many as they should. The simpler it becomes to author and execute those tests, the more likely it becomes a test will actually be run before an application gets deployed in a production environment.

October 4, 2023
Tricentis Adds Device Cloud Service to Scale Mobile App Testing

Tricentis has extended its cloud service for testing applications to include the ability to emulate multiple classes of mobile computing devices.

Mav Turner, CTO for DevOps at Tricentis, said the Tricentis Device Cloud (TDC) eliminates the need for organizations to acquire multiple physical devices to test mobile applications at scale.

Capabilities include the tracking of key performance indicators (KPIs) such as blurriness, slow downloads, HTTP redirects and network or connectivity issues that adversely impact the user experience, in addition to performance issues such as load times and excessive consumption of CPU, memory and battery resources.

The grid-based service also uses machine learning algorithms to apply artificial intelligence (AI) to monitor application performance and detect video and audio quality issues.

Overall, the goal is to make it simpler for DevOps teams to build and deploy mobile applications faster and make sure they are thoroughly vetted rather than waiting for feedback from end users to discover issues that could have been easily identified before an application is deployed, said Turner.

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

In the meantime, the debate over how far application testing should be shifted left toward developers continues to rage. In theory, the more developers test applications themselves the fewer issues there should be once an application is deployed in a production environment. However, there is still going to be a need for dedicated testing teams to conduct tests across multiple stages of the application development process to ensure quality. In fact, as more routine tests are conducted by developers, it should free up application testers to focus more on edge use cases that might otherwise not be tested before being encountered by end users, noted Turner.

Testing mobile applications is more critical than ever because the cost of failure—in terms of impact on the business—is now much higher as more organizations depend on software to drive business processes. The challenge is that many of these app developers don’t have a software engineering mindset, so they need testing platforms that automatically create and run tests on their behalf in a way that can be customized for specific use cases. In time, many of those tests will be automatically created by artificial intelligence (AI) models that should reduce the cognitive load required to manage the testing process, added Turner. In effect, application testing will soon be democratized by AI, he noted.

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

August 7, 2023