Lauren Payne

Meeting the AI Software Quality Challenge: SmartBear’s Testing Portfolio

April 27, 2026 by Lauren Payne

AI-accelerated development has fundamentally changed how software is built, and across the industry, its impact on quality is already measurable. In SmartBear’s Closing the AI software quality gap study, we found nearly 70% of software professionals report application quality is declining as AI speeds up code generation, with development velocity increasingly outpacing teams’ ability to test effectively.

This is not a future risk or a theoretical concern. The gap between code generation speed and testing capacity continues to widen, creating an unsustainable dynamic. Teams face an impossible choice: either bottleneck development to maintain testing rigor or accept degraded application quality as development races ahead unchecked. But what if that tradeoff isn’t actually necessary?

Application integrity: The new standard for AI-era quality

Application integrity is the continuous, measurable assurance that your software works as intended at AI speed and scale. When code generation wildly outpaces application validation, maintaining integrity becomes impossible without sacrificing the velocity gains that AI-accelerated development promises. The consequences of compromised application integrity are severe: regulatory fines, brand damage, customer loss, and revenue impact. SmartBear addresses this challenge with the SmartBear Application Integrity Core™ – unifying the system of record (API catalog and test repository) with MCP tools and agentic workflows that empower developers and AI agents to deliver software that just works. Whether testing runs in cloud-native environments, on-premises infrastructure, or is managed directly within Jira, teams can continuously validate applications while maintaining control over quality as development accelerates.

SmartBear’s testing portfolio: Built for AI velocity

BearQ™: Autonomous QA for the next generation

SmartBear BearQ represents a fundamental shift in how testing keeps pace with AI-accelerated development. This agentic QA system operates at the highest levels of autonomy, serving as an exploration and testing analog to autonomous coding tools. BearQ thinks and tests like a real user, exploring applications autonomously and discovering flows rather than following pre-determined scripts. It adapts continuously as applications evolve, automatically updating tests without manual rewrites while maintaining full human visibility and control.

Reflect: Vision-based AI automation for modern applications

Reflect is a cloud-native test automation platform that uses vision-based AI to create and maintain tests that remain stable as applications evolve. By interpreting the UI the way users do, Reflect removes dependency on brittle selectors, enabling automation across web, mobile, and API workflows within a single platform. Teams can generate tests agentically or through natural language prompts, with built-in self-healing that automatically adapts to UI changes, reducing maintenance overhead while expanding coverage.

TestComplete: Enterprise desktop and web UI automation

TestComplete provides deep automation support for complex desktop applications, internal web systems, and legacy frameworks that modern cloud-first tools cannot reliably support. Its ability to run in secure, on-premises environments makes it essential for organizations with compliance requirements or specialized UI frameworks. Supporting multiple automation approaches – from record-and-replay to full scripting – TestComplete enables teams with different skill levels to work within the same system. Advanced hybrid object recognition combines property-based detection, text extraction, and vision AI to interact accurately with complex interfaces.

QMetry: Enterprise testing platform for scalable QA

QMetry is an enterprise test management platform that unifies performance, visibility, and automation in a single system designed to handle millions of test cases without performance degradation. As a centralized testing system of record, QMetry provides real-time visibility, audit-ready traceability, and customizable reporting across the entire organization. AI-driven capabilities streamline test creation and maintenance, with automated test case generation reducing creation time from 30-60 minutes to under 60 seconds. Built-in compliance features support regulated environments with flexible deployment options.

Zephyr: Jira-native testing for agile teams

Zephyr integrates testing directly within Atlassian Jira workflows, enabling teams to create, execute, and track tests alongside user stories, requirements, and defects without switching tools. This Jira-native approach provides end-to-end traceability across planning, execution, and validation while maintaining performance even as test libraries grow. Rovo agent skills enable natural-language queries to evaluate test coverage and assess release readiness, while MCP server capabilities extend Zephyr beyond Jira for more flexible workflows.

Swagger: Spec-driven API testing and contract validation

Swagger enables teams to design, test, document, and govern APIs using OpenAPI as a shared source of truth. By deriving testing directly from API specifications, Swagger reduces drift between design and implementation while enabling both functional validation and contract testing. Swagger Functional Testing validates endpoints against OpenAPI specifications, ensuring requests, responses, and data structures conform to defined contracts. Swagger Contract Testing verifies that API changes don’t break downstream consumers, critical for distributed and microservices-based architectures.

ReadyAPI: Comprehensive API testing for real-world conditions

ReadyAPI enables teams to validate API behavior across functional and performance scenarios while simulating dependencies through service virtualization. Supporting REST, SOAP, GraphQL, JMS, and other protocols, this on-premises platform allows functional tests to be converted into load tests without rebuilding scenarios. LLM-driven test generation creates and validates complex test cases with large data volumes using no-code, prompt-based workflows. Service virtualization simulates dependent systems, enabling testing when external services are unavailable – especially valuable in complex environments requiring infrastructure control.

A testing system that scales with modern development

The SmartBear testing portfolio addresses the fundamental challenge facing development teams: maintaining application integrity as AI accelerates code generation. Individual tools solve specific testing challenges across UI automation, API validation, and test orchestration. Together, they create a unified testing system that scales with AI-driven development velocity.

When testing infrastructure operates as a coordinated system rather than isolated tools, teams gain the ability to validate applications comprehensively without sacrificing speed. Automation scales without becoming fragile. API changes are validated before reaching consumers. Testing coverage remains aligned with development rather than trailing behind it. The result is not a choice between speed and quality – it’s the ability to deliver both while maintaining the application integrity that modern software demands.

Author

Rob McNeil Senior Manager of Product Marketing

Rob is a Senior Manager of Product Marketing focused on defining the go-to-market strategy for SmartBear’s portfolio of software testing products. He is passionate about engaging with customers and bringing their voices into product strategy so that feature launches align with real market needs. He has been with SmartBear for four years, with his more recent projects centered on researching the impact of AI, including how it is bringing significant changes to software developers and testers, and launching new generative AI and agentic AI features to meet the demands of the evolving development landscape.

SmartBear are Gold Sponsors in EuroSTAR 2026. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

The AI Velocity Paradox: Why Your Test Data is the New Bottleneck

April 22, 2026 by Lauren Payne

Generative AI has dramatically accelerated software development. Ideas that once took weeks to turn into working code can now be prototyped in hours. AI tools generate code, suggest tests, analyze logs, and help engineers iterate faster than ever. According to McKinsey’s research on developer productivity, AI-assisted teams can complete coding tasks significantly faster, with some organizations reporting development velocity improvements of 30 to 40% in AI-supported workflows.

But something interesting is happening in many organizations: even as coding accelerates, releases still stall. Teams wait not for code to be written, but for confidence that it is safe to ship. In many cases, the blocker is not the tests themselves. It is the data behind them. In the AI era, test data has quietly become the new critical path to quality.

Consider a typical enterprise team using AI coding assistants. Development velocity improves immediately. Features move from idea to code faster than ever. But releases still slip because QA teams must wait days for masked datasets or environment refreshes. The development bottleneck is gone, but the testing bottleneck remains.

The Bottleneck is Shifting in the SDLC

AI now supports multiple stages of the software lifecycle, reducing work that once took days to mere hours. Yet validation still takes time. Teams can generate tests quickly, but they often wait on environments, data provisioning, and compliance approvals before those tests can run.

As development accelerates, constraints move downstream toward validation. Inside QA, test data and test environments are where projects most often slow down.

Why Test Data is Uniquely Hard

Accessing usable test data is rarely simple. Industry research consistently shows that testers spend up to 30 to 40% of their time searching for or preparing data rather than executing tests. Manual provisioning of test datasets can take days or weeks. That cadence does not match modern CI/CD pipelines or AI-assisted development.

When teams cannot access realistic data, test coverage suffers. Edge cases go untested, business workflows behave differently than they do in production, and defects slip through into live environments. These inconsistencies make tests unreliable and environments unpredictable. Furthermore, if automation frameworks and AI-driven testing tools rely on incomplete data, the automation itself becomes unreliable.

The AI Twist: More Code, More Testing Pressure

Agentic AI will generate more code changes and more test cases than any previous tooling. This increases the validation volume required in every build.

If teams run AI-generated tests on unrealistic datasets, the results can be highly misleading. A build may pass in the lab while still hiding defects that appear in production. “Green builds” do not always mean safe releases. Without production-like data, testing becomes a simulation detached from reality.

Why Traditional Test Data Management Breaks Under AI Velocity

Feature	Traditional Test Data Management	Modern Test Data Management (AI Era)
Provisioning Model	Centralized teams, ticket-based requests	Self-service, on-demand generation
Data Source	Full database copies (heavy, slow)	Multi-source data automatically masked, blended, and pushed in QA/UAT environments
Speed	Days, weeks, or months	Minutes (self-service UI or integrated into CI/CD pipelines)
Architecture Fit	Monolithic legacy systems	Spans legacy, SaaS, and micro-services
Compliance / PII	Manual masking that differs by database, inconsistent enforcement	Automated, centralized PII masking and synthetic data, built-in by design

What Good Test Data Looks Like Today

To support modern testing, data must meet three strict criteria:

Realistic: Datasets must reflect production behaviors, edge cases, and real business workflows.
Compliant: Personally identifiable information (PII) must be protected. Using synthetic test data (realistic but fictitious datasets generated to mirror production patterns) and advanced masking techniques helps preserve useful data characteristics without exposing real user details.
Consistent: Enterprise applications span multiple systems. Test data must reflect accurate relationships across services, platforms, and applications.

Platforms like K2view approach this by organizing enterprise data around business entities such as customers, policies, or orders using patented Micro-Database technology. This allows teams to provision complete, consistent datasets that reflect real user journeys across multiple systems while maintaining referential integrity. Instead of stitching together multiple tools, teams combine data masking, cloning, and synthetic generation within one platform.

Don’t Let Test Data Become the New Bottleneck

The bottleneck has moved, but it hasn’t disappeared. Teams that recognize test data as infrastructure — not an afterthought — will be the ones who actually realize the promise of AI-accelerated development. The rest will keep explaining why fast code still means slow releases.

Author

Amitai Richman, Director of Product Marketing K2view Test Data Management

Amitai Richman is Director of Product Marketing at K2view, where he leads GTM strategy for enterprise AI and test data management solutions. He specializes in translating complex data and agentic AI capabilities into clear business value for enterprise buyers, with a particular focus on TDM, synthetic data generation, and AI-ready data infrastructure. Amitai has presented on AI in software testing at industry forums across Europe and the US, and is a recognized voice in the product marketing and QA communities.

Test data as a managed asset: the foundation for scaling QA in complex environments

April 20, 2026 by Lauren Payne

Automation has become the big bet for QA teams. Yet in many organisations, the expected growth never materialises.

In 2025, we analysed the reality of more than 150 companies across Europe and Latin America to understand what is holding that evolution back. The conclusion was clear: automation is a priority, but it does not scale.

Over 66% of teams say expanding automation is their main objective. However, more than 50% do not exceed 40% automated coverage, and 34% identify the lack of suitable test data as the primary obstacle.

The pattern repeats itself. When test data is not managed as a strategic asset—consistent, secure, and available on demand—automation loses stability, cycles slow down, and risk increases.

The Problem Isn’t Automation

Most improvement initiatives focus on frameworks, tools, or pipelines. Organisations invest in automation expecting a rapid leap in efficiency.

Yet the study points to a different reality: 32.7% of teams say test data management consumes a significant share of their effort, and 22% still create test data manually.

The symptoms are clear:

Tests fail due to inconsistencies in context, not because of real defects.
Environments take days or weeks to become ready.
Teams rely on technical experts to build complex queries.
Scenarios are not repeatable across QA, UAT, and production.

The result is reactive testing that is difficult to scale.

From Operational Task To Managed Asset

Scaling QA doesn’t require more automation scripts; it requires changing how we think about data.

Test data managed as an asset is not simply data that is available—it is an all-powerful dataset:

Functionally and technically consistent.
Secure by design.
Available on demand, without organisational friction.

This means moving from a craft approach to an industrial one.

The approach proposed by icaria TDM is not about adding more manual processes. It is about structuring test data management as a complete system: coordinated mechanisms, metrics, and traceability.

The journey towards that maturity is built in layers.

1. Built-in security: removing the fear of realism

In complex environments, the first blocker is often regulatory. Under pressure to test with realistic data, many organisations resort to large-scale production copies. The problem is that this shortcut creates risk and slows progress.

Embedding data masking into the flow with icaria TDM changes the conversation. Automated discovery of sensitive data, dissociation policies, and multi-technology execution turn privacy into a structural capability.

The question stops being, “Can we use this data for our tests?” and becomes, “How do we design it so it’s useful and safe?” That unlocks the next layer.

2. Designed consistency: less volume, more control

Once risk is addressed, the next challenge emerges: scale.

Cloning entire databases is not the same as realism; it is cost and inefficiency.

Intelligent subsetting makes it possible to define complete functional domains—for example, a customer and all their relationships across different systems—and extract only what is needed while maintaining referential integrity.

The impact is immediate:

Environments available consistently.
Significant reduction in infrastructure.
Agile reproduction of real incidents.
Greater stability in end-to-end testing.

Here, data stops being a copy and becomes a controlled construct.

3. A shared model: from local hero to system

In the previous layer, data still depends on complex queries maintained by specific individuals. The organisation does not have a system; it has internal dependencies.

Introducing data archetypes makes it possible to define, consistently, what each business term means from a shared perspective of business and testing. icaria TDM allows these archetypes to be defined and enables teams to find suitable data instances for each test. The associated search tool turns discovery into a standardised, reusable, and traceable process.

Knowledge stops being owned by a few and becomes shared. And with that, data begins to behave as an organisational asset rather than an individual one.

4. Integrated into the flow: when data enters CI/CD

The real step change happens when data is integrated into the pipeline.

Requesting a dataset on demand, injecting it automatically before the test run, and validating the outcome from a data perspective removes the most common bottleneck in continuous testing. Automation is no longer limited by data availability—data moves at the same pace as code.

5. Delivery automation: designing data inside the test

In mature organisations, data is not hunted down before execution; it is designed as part of the test case.

Each test includes the conditions its dataset must meet. The system finds or generates it, protects it, delivers it, and restores it whenever needed for reuse.

At this layer, rework is reduced, instability is eliminated, and every test becomes independent—the point at which data definitively becomes a strategic asset.

From operational maturity to strategic impact

When test data management reaches a structured model, the impact stops being purely technical and becomes financial and strategic.

With icaria TDM, test data stops depending on manual processes and becomes a measurable, repeatable organisational capability. This translates into tangible benefits:

Faster delivery cycles through on-demand provisioning.
Elimination of rework caused by data inconsistencies.
Reduction of environment preparation times from weeks to minutes.
Realistic, stable coverage, even in complex multi-system architectures.
Reduced regulatory risk through integrated masking and compliance.

When data stops being an operational problem, the benefits start to show across the organisation.

icaria TDM: turning test data into competitive advantage

In projects where icaria TDM has been implemented, organisations have observed measurable outcomes such as:

Reductions of up to 90% in infrastructure costs associated with mass cloning and oversized environments.
ROI above 300%, driven by removing idle time, rework, and automation blockers.
More autonomous teams, able to focus on higher-value work instead of preparing or fixing data manually.

These results do not come simply from adopting a tool, but from adopting a structured model where test data is managed as a product, with on-demand provisioning, traceability, and integrated compliance.

icaria TDM enables that shift: a coordinated system in which data is consistent, secure, repeatable, and available exactly when needed.

In complex environments, scaling QA is not only about automating more—it is about turning test data into critical infrastructure that accelerates delivery, reduces risk, and creates competitive advantage.

Author

Ana Victoria Rodríguez

Ana Victoria Rodríguez has spent more than seven years working on Test Data Management (TDM) methodologies across software development and testing programs. Her experience includes designing TDM platforms for mission-critical applications in complex system landscapes, emphasizing repeatability, governance, and data protection.

iCaria are Exhibitors at EuroSTAR 2026. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

Currys achieves 4X faster release cycle with BrowserStack AI-Powered Test Management

April 17, 2026 by Lauren Payne

Introduction

Currys, a leading UK omnichannel retailer, is committed to delivering seamless digital experiences. With a vast online and offline presence, ensuring high-quality software releases is critical. However, fragmented testing, manual test management processes, and limited visibility into testing efforts slowed development cycles. To overcome these challenges, Currys adopted BrowserStack Test Management, resulting in faster releases, improved test coverage, and streamlined quality assurance.

The Challenge

Fragmented testing and limited visibility

When Gregg Ward, Principal Quality Assurance Manager, joined Currys, he found a fragmented testing process. Teams operated in silos, using multiple different tools for testing and Excel spreadsheets stored on individual computers to manage test cases. The lack of a centralized system led to inefficiencies, making collaboration difficult and slowing down issue resolution.

Without real-time visibility, their team spent more time chasing information than solving problems. Gathering data took three times longer than fixing issues, and the deep technical knowledge required made onboarding and collaboration challenging. Testing remained a black box for stakeholders, impacting release cycles and innovation. Test creation was also time-consuming, impacting release cycles and slowing innovation.

“We needed a single source of truth that would provide real-time visibility to everyone involved, from developers to stakeholders. We wanted to standardize transparency and real-time results, and BrowserStack ticked all those boxes,” Gregg says.

The Solution

BrowserStack AI-Powered Test Management

Currys moved to BrowserStack Test Management for its seamless integration, intuitive interface, and AI capabilities. The platform provided a centralized hub for test creation, execution, and tracking, enabling both technical and non-technical users to access critical information effortlessly.

Migrating from legacy systems was a major shift. With 40 years of legacy platforms and over 200,000 test cases in massive Excel files, they were concerned about losing critical data. But BrowserStack supported them every step of the way, providing hands-on assistance. The result? Not a single test case was lost—everything was intact. With BrowserStack’s support, Gregg’s team onboarded 120 users and imported 60,000 test cases from an existing Test Management tool, including a project with 22,000 test cases migrated in under five hours.

“We haven’t lost a single test case. Everything is there. BrowserStack Test Management lets you set up field mappings to match however your legacy test files were structured, and it’s incredibly flexible,” Gregg says. This ensured that Currys’ QA teams could continue their work without disruption, maintaining the integrity of their testing data while benefiting from a more streamlined and efficient test management system.

Having migrated their existing test cases, Currys leveraged BrowserStack’s AI capabilities to accelerate test creation. “We recently introduced BrowserStack AI to our testing teams across all areas of Currys, and the impact was immediate. The increase in coverage was fourfold, but the real advantage was the speed at which we could create test cases directly from our UI, Jira, or Confluence,” Gregg explains. AI-driven automation reduced manual effort and encouraged broader, more innovative test coverage.

The platform’s dashboards and reporting further improved issue detection. “Dashboards in BrowserStack are incredibly useful. They provide an easy-to-follow information path that we can share with stakeholders, making it simple to highlight key insights and zoom into the details when needed.”

With Jira integration enabling two-way visibility, developers and stakeholders could access real-time test session data without switching tools. “With the Jira integration, people can see what test sessions are happening, what’s failed, and the last time a test was run—all from a single ticket,” says Gregg

The Impact

Faster releases, greater confidence, and improved collaboration

With BrowserStack Test Management, Currys transformed its QA efficiency and dramatically increased release cadence. Previously, releases followed a rigid, waterfall-style process, deploying just once per sprint. Now, with AI-driven test management, the team releases up to four times per sprint, with some teams shipping updates every few days.

“Our average deployment cycle has increased fourfold with BrowserStack. Where we were releasing once in a two-week sprint, we’re now deploying four times per sprint, with some teams releasing updates as frequently as every few days,” Gregg states.

Beyond speed, collaboration and transparency have improved significantly. Teams now have real-time access to testing data, breaking down barriers between QA and development. “BrowserStack has changed how people interact with the quality team. It’s no longer just about testers pushing back on breaking changes. Everyone sees the same information at the same time, which promotes real-time discussions and cross-team collaboration,” Gregg emphasizes.

By consolidating all testing tools into BrowserStack, Currys eliminated inefficiencies caused by fragmented workflows. Previously, test management was scattered across multiple platforms, making it difficult to access real-time insights. Now, with a single source of truth, Currys has streamlined QA, accelerated release cycles, and improved software quality. “It’s a game-changer. Everyone knows where to look for information, everyone understands the quality of what we’re producing, and that’s all thanks to BrowserStack Test Management,” Gregg concludes.

If you would still like to know more, BrowserStack provides AI enabled products and agents across the testing lifecycle. Reach out to us here.

Author

Ankit Jain Senior Director – Product Management, BrowserStack

Ankit is a Senior Director of Product Management, spearheading the fastest growing Test Management and Scanner product lines at BrowserStack. With over 20 years of experience, as a Product Leader, Founder, Investor, and Developer, he has led global teams and driven strategic initiatives, specializing in 0-to-1 development, growth, monetization, and scaling across startups, hyper-growth companies, and public enterprises.

BrowserStack are Platinum Sponsors in EuroSTAR 2026. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

The ROI of Test Data Done Right

April 15, 2026 by Lauren Payne

Why every minute your team spends waiting for test data is costing you more than you think.

If you’ve ever watched a testing sprint grind to a halt because the right dataset wasn’t available, or had a release delayed because test data was stale, inaccurate, or non-compliant, then you already know this problem intimately. What you might not have done yet is put a number on it.

That’s what I want to do in this post. I want to talk honestly, as a fellow practitioner, about the hidden costs that poor test data management is piling onto your team, your delivery timelines, and your organisation’s bottom line, and what a smarter approach can unlock.

Sound familiar? The challenges most testing teams share

Let’s start with a quick show of hands. How many of these ring true for your team?

You’re waiting on another team or a DBA to provision test data before you can start a sprint.
Your tests are failing in ways that turn out to be data issues, not code issues — and you only find out late.
You’re using copies of production data that haven’t been properly anonymised, and that GDPR audit feels closer every day.
Entire databases are being duplicated across environments, silently inflating your infrastructure bill.

These aren’t edge cases. They’re the day-to-day reality for a huge number of testing teams. And because they’re so normalised, they often fly under the radar when it comes to calculating the true cost of software delivery.

The costs hiding in plain sight

Let’s make the invisible visible. Here are the three biggest cost drivers that inefficient test data management introduces:

1. Lost time — and lots of it

When testers have to wait for datasets, they don’t sit idle — they context-switch to other work, lose momentum, and then have to reload context when data finally arrives. Multiply that across a team over a quarter and you’re looking at significant lost throughput. Time is the one thing you can’t get back.

2. Infrastructure bloat

Full database copies spun up for each test environment might seem like the easiest path, but they come with a storage and compute price tag that compounds over time. Subsetting and virtualisation techniques can dramatically reduce this overhead — but only if they’re actually in place.

3. Compliance risk — and the fines that follow

Using real customer data in test environments without proper masking or anonymisation isn’t just a technical problem — it’s a legal one. GDPR and CCPA violations can result in substantial fines, but the reputational cost of a data breach originating in a test environment can be even more damaging.

The cost of doing test data badly isn’t abstract. It shows up in your sprint velocity, your infrastructure invoices, and your compliance risk register.

What good looks like: the benefits of getting it right

The flip side of all those costs is a compelling ROI story. When test data provisioning is well-organised, the benefits compound quickly:

Speed and autonomy: Teams can provision their own test data independently, without waiting on other departments.
Better quality: With realistic, representative data, bugs are caught earlier — when they’re cheapest to fix.
Reduced risk: Automated masking and anonymisation remove the compliance risk almost entirely.
Lower infrastructure costs: Subsetting means you’re not spinning up multi-terabyte environments for every test cycle.

One organisation I know of — with distributed teams across multiple locations — cut their test cycle time by 25% simply by restructuring how test data was provisioned. That’s not a marginal gain; it’s a meaningful competitive advantage.

Collaboration is the piece most teams miss

Here’s something that doesn’t come up enough in conversations about test data: it’s not just a tooling problem, it’s a collaboration problem.

In modern software teams, developers, testers, and data engineers all have a stake in test data — but they’re often working in silos. A developer needs a specific dataset to reproduce a bug. A tester needs anonymised data that mirrors production. A data engineer is trying to keep environments consistent. When these groups aren’t working from a shared, standardised solution, you get duplication, inconsistency, and friction.

The best test data platforms aren’t just technical tools. They’re shared infrastructure that lets everyone access what they need, when they need it, without stepping on each other’s toes.

“Test data is a shared resource. When it’s treated that way, teams move faster — and they argue less.

Why now is the right time to act

The pressure to ship faster, more securely, and more reliably isn’t going away. If anything, it’s intensifying. And the organisations that will keep pace are the ones that treat test data as the strategic asset it is — not an afterthought that gets sorted out at the last minute.

Investing in test data management isn’t a nice-to-have. It’s infrastructure for speed. And like any infrastructure, the longer you wait to put it in place, the more technical debt accumulates in the meantime.

If you’re making the case internally, the numbers are on your side: faster cycles, lower storage costs, reduced compliance risk, and less time lost to data-related blockers. That’s a story that resonates with engineering leads and CFOs alike.

Let’s keep the conversation going

Test data management is one of those topics that testing professionals instinctively understand, because they live with the consequences of it every day. What I hope this post has done is help frame those lived experiences in terms of measurable impact.

If you’re wrestling with any of the challenges above, or if you’ve found solutions that work for your team, I’d love to hear about it. Drop a comment, reach out, or — even better — join us at the webinar below.

📣 Join Our Upcoming Webinar

The Hidden Cost of Test Data Management

07-05-2026 | 15.00 (EST) | Free to attend

Join us for a practical session where we dig into the real — often invisible — costs hiding in your test data process. We’ll cover how teams are wasting time, inflating budgets, and increasing compliance risk without realising it, and what you can do about it. 👉 Sign up here: www.datprof.com/webinars/the-hidden-cost-of-test-data-management

Author

Maarten Urbach

I believe that test data is the hidden accelerator of quality software. Too often, QA teams struggle with incomplete datasets, developers lose time creating workarounds, and IT management faces rising costs and compliance risks.
For more than nine years, I’ve worked with organizations worldwide to help them overcome these challenges. As a co-owner at DATPROF, I partner with QA, Dev, and IT leaders to implement smart solutions for test data provisioning, masking, generation, and analysis.

DATPROF is an exhibitor at EuroSTAR 2026, join us in Olso.

Testing AI Agents: What QE Teams Need to Unlearn Before They Can Get This Right

April 13, 2026 by Lauren Payne

Run the same AI agent with the same input ten times. You will get ten different results.
Sometimes subtly different. Sometimes wildly.

That single fact breaks almost everything traditional QA was built on.

LangChain’s 2026 State of Agent Engineering report surveyed 1,300+ professionals. The
findings are stark: 57% of organizations now have AI agents in production. Quality is the number one barrier to deployment, cited by 32% of teams. And only 52% have any evaluation system in place.

AI Agents Are in Production, but Evaluation Is Still Maturing

Do the math. Roughly half the organizations shipping agents to production have no structured way to know if those agents work reliably. For enterprises with 10,000+ employees, the top concern is not cost or speed. It is hallucinations and output consistency.

Gartner’s 2025 Hype Cycle placed AI agents at the Peak of Inflated Expectations, noting that multi-agent workflows and model non-determinism may trigger cascading failures.

That confidence gap is where QE teams should be rushing in.

Why the Input–Output Contract No Longer Holds

Traditional QA lives on a simple promise: given input X, expect output Y. AI agents break that promise by design. A customer service agent might resolve the same complaint through five different valid approaches. A coding agent might fix a bug with three different architectures. The output varies. The path varies. Both can be correct.

You cannot write an assertion that says “the response must equal this exact string.” You cannot build a regression suite expecting identical behavior across runs. And you cannot rely on pass-fail verdicts when the definition of “correct” depends on context, tone, and user intent. This is not a tooling problem. It is a thinking problem. And it demands that QE teams unlearn some deeply held assumptions about what testing looks like.

Define Behavioural Boundaries, Not Exact Outputs

The most effective teams testing AI agents have made a counterintuitive shift: they stopped checking exact outputs and started defining behavioural bounds.

Anthropic’s engineering team addressed this in their guidance. They recommend evaluating the quality of the final output rather than the exact steps taken to reach it. Agents often arrive at effective solutions through alternative paths. If evaluation frameworks reject those paths, the test suite becomes brittle instead of robust.

Practically, this means asking different questions. Did the agent call the correct tools? Did it stay within policy guardrails? Did it reach a valid end state? Did it handle edge cases without hallucinating?

Simulate Users, Not Just Inputs

Structured simulation frameworks help reduce production agent failures. The approach is simple: test agents against diverse user personas, communication styles, and edge cases before deployment.

A customer service agent that handles polite requests perfectly might collapse with ambiguous or frustrated users. A voice assistant tested only with clear enunciation will fail in noisy real-world environments. Testing AI agents means testing the full range of human unpredictability.

This is exactly the problem TestMu AI’s Agent-to-Agent Testing platform was built to solve. It uses specialized AI agents to simulate diverse personas, generate thousands of test scenarios, and validate how your agent handles conversation, reasoning, and context across real-world conditions.

The concept of using agents to test agents sounds recursive, but it is the only approach that scales to match the complexity of these systems.

Quality Is a Continuous Signal

Many teams approaching agent testing are moving beyond the idea of quality as a one-time, pre-release checkpoint. Instead, they treat it as an ongoing signal.

Production logs can inform new test cases. Real user interactions can expand scenario libraries. Evaluation can run continuously as agents evolve, helping teams adapt as behaviour changes over time.

LangChain’s data confirms this shift: 89% of teams have implemented observability for their agents. But observability without structured evaluation is just logging.

The winning practice combines automated monitoring to flag anomalies with human reviewers making judgment calls on ambiguous cases. Platforms like KaneAI support this continuous model. When test authoring, execution, reporting, and test management live in one unified system, the feedback loop from a production anomaly back to the relevant test scenario becomes fast and actionable, tight enough to drive real quality improvements.

The Discipline Is Being Rewritten

Quality engineering is expanding. As AI systems introduce probabilistic behavior, tool
orchestration, and adaptive workflows, the craft naturally grows more complex. Engineers who understand both testing fundamentals and AI system mechanics are well positioned to navigate that shift.

For teams already practicing strong QE, the shift is less about starting from scratch and more about refining the lens.

Author

Mudit Singh Co-Founder at TestMu AI

With over a decade of experience building and scaling
software products, he has helped shape quality engineering and AI-driven testing strategies that empower engineering teams to ship reliable software faster. His work spans product strategy, AI-native quality engineering, and community-led innovation, bridging the gap between human expertise and autonomous systems.

TestMu AI are Gold Sponsors at EuroSTAR 2026. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

How the Testing Discipline Adapts to Conform with Agentic SDLC

April 3, 2026 by Lauren Payne

Disclaimer: this article is 100% human effort, no LLMs were leveraged while writing it.

Since the end of 2022, people have been using LLMs, first for fun, and then for work related activities. 2023 was the year of doubts, with the majority of people calling this period the “AI-hype Era”. Many were afraid to even try out any of these LLMs. Then in 2024 we started hearing about successful results of AI adoption programs and saw AI-native solution adoption providing valuable assistance, primarily in coding tasks.

In 2025, the scenery changed again. We started hearing more about the pitfalls of GenAI transformation projects, the emerging risks and challenges, and how one could potentially bridge these gaps and avoid hurting their business. Most people were still cautious, but they were also curious.

Moving on from simply using chatbots, the natural next step was leveraging a code
assistant inside an IDE. This is a great way to boost your output in test automation, but
without the right context, i.e. proper test data and agentic knowledge of your enterprise systems, the code produced could turn out to be generic and not tailored to what you need.

The first answer to that problem was RAG (Retrieval Augmented Generation), and then more recently the MCP (Model Context Protocol). The former enables you to leverage additional data – custom embeddings and datasets – and effectively expand what your LLM can access. The latter provides communication between LLMs and Agentic systems with external systems such as project or test management tools.

Although the tooling is important, the human aspect needs to be considered too, and in
fact, is the biggest factor in successful AI transformation programs and adoption.
Now in 2026 we have an even clearer picture of implementing AI-native business and
operational solutions across almost every industry. The first thing to highlight is that the number one discipline where we see success in AI adoption is:

Testing!

This may come as a surprise to you, my fellow Testers, as usually Testing often seems to be an afterthought. Dev and DevOps continue to prioritize coding and delivering fast value. So how come Testers are the forerunners now?

The recipe is simple: Testers have a critical, methodical, and investigative mindset. They
provide unfiltered feedback and really care about the products they are working on. Testers also have a deep understanding of software and are comfortable with new, possibly unfamiliar technologies, both as users and as technical professionals.

Let’s break down, how that aligns with GenAI:

Prompt Engineering became a globally recognized role in 2023, and it became mandatory skill to pick up for teams working in an Agentic SDLC
For prompts, you need to be descriptive and have a thorough understanding of what you need the LLM to accomplish or provide
Testers already have the analytical mindset due to requirements analysis
Testers already understand what the end-users and the business are looking for
Testers already work closely together with developers
Test automation code needs to be integrated in CI/CD pipelines, and quality gates need to be defined at different stages of the delivery

EPAM realized that Testers are the Swiss Army Knives of the Software world. Testers make the perfect Prompt Engineers, as they possess all the required prerequisites to pick up the necessary new skillsets fast to excel in this AI world. And then, they can be the perfect catalysts and support system for pursuing broader AI adoption across an organization.

Example Agentic SDLC phases, AI assistants and benefits of using AI

Agentic SDLC is all about bringing AI-assistance to every stage of development, QA and operations, be it requirements analysis, user story creation, developer’s review of user stories, test case definition, code change impact analysis, test orchestration, or vibe
coding of product code and test automation code.

For each of those tasks, a pipeline of AI agents can provide task level productivity gains. The more use cases you identify to augment with AI, the more overall team productivity gains can be realized. For that, you need to investigate applicable disciplines holistically and ensure that each team member is engaging with the implemented AI solution while developing mastery (Note: A number of AI orchestration and collaboration platforms, like EPAM’s EliteA, have built-in tools to help managers track adoption and skill growth). That’s when adoption can accelerate, and your teams can together ensure an impactful ROI (return on investment) on GenAI adoption programs.

That’s when QA people come into the picture again: We like to set up QA metrics to see trends and be able to course correct when the ship is navigating in the wrong direction. AI solutions are software solutions as well. Usage of these needs to be carefully observed, and course corrected at times. Testers know how to do that and can help teams and organizations avoid waste through proactive, predictive, and preemptive monitoring.

Example agentic eco-system leveraging MCP and ELITEA’s system connectors

To provide better insight, let us give you numbers from one of clients, an insurance
payment platform provider. We measured up to 90% task-level productivity gains on
performance tests results analysis, and on requirements analysis. Test case generation
and orchestration provided 75%, while user story and user guide creation provided 67%
gains. Agents assisted vibe coding enabled developers and test automation engineers to
spend around 40-45% less time on coding.

These numbers may look high, but don’t forget that these were task-level gains. The teamlevel gains were between 27.8% and 31.8%, as not all the tasks of business analysts,
developers, and testers were AI-assisted. As highlighted above, the more use cases
augmented with AI, the more disciplines adopting those solutions, the higher the overall productivity gains are.

Overall, there is an incredibly positive light and exciting opportunity for our beloved
discipline in this new era. But it’s important that You, as a Tester, start adapting to and
working with this new style of delivery, or you risk being left behind. If you are unsure where to start or how, then reach out to us, we are always happy to help.

Visit EPAM at booth 15 at the EuroSTAR conference. Come on over and say hello, and let’s seize these new AI opportunities together!

https://www.epam.com/services/engineering/quality-engineering

Author

Péter Földházi Quality Architect, AI & Game QA Consulting, North America

Péter was first involved with QA as a beta tester of DOTA in 2006. Since joining EPAM in 2012, he moved towards test automation and is currently working in the USA as a Quality Architect.

He is leading Game Testing Consulting and GenAI adoptions in the Americas.

Péter has authored two ISTQB syllabi: Test Automation Engineering & Test Automation Strategy. He also invented two test automation methodologies: the Flow Model Pattern and the Tri-Layer Testing Architecture, the latter published as a white paper by the PNSQC. Péter has been one of the review board members of the HUSTEF since 2015.

Péter is a regular keynote and tutorial speaker on conferences such as STARWEST, STAREAST, and SauceCon. He used to be a guest lecturer at 3 Budapest based universities: Óbuda, Pázmány and the ELTE. Brewing beer and planting chilis are some of his hobbies.

Editor: Ted Weil – Marketing Manager, TestIO & EPAM Testing Practice

EPAM are Exhibitors in EuroSTAR 2026. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

How AI Is Changing Test Case Creation

April 1, 2026 by Lauren Payne

Why test case creation is under pressure

In software development, speed is no longer a competitive advantage — it is an expectation. Teams release continuously, requirements evolve rapidly, and documentation quality varies. Yet one constant remains: quality must be reliable.

Test case creation sits at the heart of this challenge. It translates requirements into structured validation, turning ideas into verifiable outcomes. But under increasing time pressure, this critical step often becomes a bottleneck. Requirements evolve rapidly, documentation quality varies, and the window for careful analysis keeps shrinking. When test cases are rushed, inconsistent, or incomplete, the consequences surface later — in escaped defects, costly rework, and delayed releases.

This growing tension between speed and quality is exactly where Artificial Intelligence begins to reshape the discipline — not by replacing testers, but by redefining how test cases are created, reviewed, and refined.

Most organizations still rely on manual test case derivation from requirement documents, user stories, or specifications. That work is important, but it comes with familiar challenges:

Time-intensive effort: Large requirement sets can take days or weeks to translate into structured test cases.
Human variability: Two testers can interpret the same requirement differently, producing uneven quality.
Coverage gaps: Under deadline pressure, edge cases and negative scenarios are often missed.
Automation friction: Manually written cases are frequently not “automation-ready” and require rework to be useful in pipelines.

This is where AI has begun to reshape the discipline, not by replacing testers, but by changing how the work is distributed.

What AI changes in test case creation

AI introduces a new operating model: machine-generated drafts plus human validation. Instead of starting from a blank page, testers start from a structured baseline created by an AI engine that has processed the underlying requirements.

In practice, the shift is not just “faster writing.” It impacts four core outcomes:

Speed: AI can generate test case drafts in a fraction of the time needed for manual extraction. That can reduce lead time from requirements to executable testing, especially helpful in early phases or short sprint cycles.

Precision: When the AI is trained and designed for requirements understanding, it can standardize structure, language, and formatting across test cases, reducing ambiguity and improving consistency.

Higher coverage: AI can systematically scan the full set of available requirements and create broader scenario sets, including negative paths, boundary conditions, and dependencies that are commonly overlooked when time is tight.

Ready for automation: If test cases are generated in a structured format, clear preconditions, steps, expected results, and stable identifiers, they become significantly easier to map into automation frameworks and CI/CD pipelines.

The key is how this is implemented. AI creates value when it produces output that is immediately usable by testers and automation engineers, not when it generates generic text that still requires heavy rework.

Introducing msg.TestcaseGen.ai: faster, more complete, automation-ready

msg.TestcaseGen.ai was built to modernize test case creation with AI, without sacrificing professional QA standards. The tool automatically generates structured test cases from requirement documentation and supports review and refinement by subject matter testers, enabling organizations to combine AI efficiency with human expertise.

From a test management perspective, the benefits align directly with what many teams need right now:

Faster test case generation: Reduce manual effort and free experts for analysis, risk assessment, and exploratory work.
More precise, consistent structure: Improve readability and reduce interpretation gaps across teams and projects.
Higher test case coverage: Systematically derive cases from the full requirements set, supporting more robust functional validation.
Automation readiness: Produce standardized test cases that can be transitioned more efficiently into automated test suites.

In short, msg.TestcaseGen.ai helps organizations move from “test cases as a documentation burden” to “test cases as an acceleration asset.”

Where it fits best: functional testing that scales

AI-based test case generation is particularly effective in functional testing, where traceability to requirements and structured step design matter most. Typical use cases include:

Structured bug testing: Creating reliable, repeatable cases that uncover functional defects.
Regression testing: Ensuring existing features still work after change, supported by consistent, maintainable test sets.
Localization readiness: Supporting coverage across language and region variants by deriving scenarios systematically from specs.

This matters because functional scope expands quickly, especially in large programs, and manual test case work rarely scales at the same pace.

Human testers still lead, AI changes what they spend time on

AI does not remove the need for skilled QA professionals. It changes where expertise delivers the greatest value.

Instead of spending most of the time on drafting and formatting, testers can focus more on:

validating intent and risk, not just steps
improving test design quality and coverage strategy
identifying missing requirements and inconsistencies
designing automation architecture and stability
ensuring test suites remain relevant over time

AI becomes a productivity layer, while testers remain the quality authority.

A practical path forward

If you are evaluating AI for test case creation, the most pragmatic approach is:

Start with a real requirement set (not a “demo” example).
Generate a baseline suite using AI.
Conduct expert review and refinement.
Measure impact on lead times, coverage and automation usability.

That is exactly the kind of practical, real-world impact msg.TestcaseGen.ai is designed to deliver, helping teams test faster, more precisely, with higher coverage, and ready for automation.

This human-plus-AI model reduces lead times, improves consistency, and increases coverage—without compromising professional QA standards.

msg will be present as an exhibitor at EuroSTAR 2026 in Oslo (June 15–18). If AI-driven test case generation is on your roadmap, msg.TestcaseGen.ai is worth a closer look. https://testcasegen.com/

Author

Tuan Truong – Head of Test Architect Product Development

Stephan Ingerberg, Head of Sales, msg Test & Quality Management

Stephan Ingerberg is a seasoned professional with over a decade of experience in the realm of software quality and digital assurance. He is a dedicated desciple of quality and testing since 2004.

Currently serving as a pivotal figure in the Test & Quality Management division of msg, responsible  for sales, customer relations and commercial aspects within central Europe. His unwavering dedication to excellence and adept navigation of software quality make him indispensable in the pursuit of digital perfection.

https://www.linkedin.com/in/stephan-ingerberg-digital-transformation

msg Test & Quality Management is an Exhibitor at EuroSTAR 2026, join us in Oslo