EuroSTAR Conference

Gen AI for Software & Quality Engineering – Elevate your possible

May 26, 2026 by Lauren Payne

When we look at the history of software engineering and quality engineering (QE), we often see significant shifts occurring every decade or so. Each has brought with it new tools, processes and methodologies that have improved the way we develop, test, and maintain software applications, and by extension, the experiences we deliver to end users.

Test automation emerged in the 1970s to accommodate growing business demands, with Agile methodologies following in the ’90s to enhance quality and security. From the 2000s, DevOps and low-code strategies enabled quicker deployment and talent attraction.

The software industry was further propelled by social, mobile, analytics, and cloud (SMAC) technologies, completely transforming it as we knew it.

Despite these advancements, challenges persist in delivering quality products at speed, managing cybersecurity risks, containing costs, reducing technical debt, and sourcing skilled talent.

Now, with the emergence of generative AI, we stand on the brink of the next major evolutionary leap in software and quality engineering. This technology merges with traditional engineering principles to significantly expedite development, testing, and maintenance processes—ensuring faster market delivery of innovative products while freeing up time for strategic initiatives.

Introducing the Gen AI Amplifier for Software and Quality Engineering: Quality Reimagined.

Accelerate QE with the Gen AI Amplifier

The Gen AI Amplifier for Software and Quality Engineering is a groundbreaking accelerator designed to optimize the Quality Engineering & Testing (QE&T) stages of the software development life-cycle (SDLC). By applying the powerful capabilities of generative AI to our industry-leading best practices, frameworks, and methodologies, we’ve crafted a uniquely amplified approach.

This accelerator is tailored to enhance the efficiency of QE&T tasks across the entire software life-cycle, from planning and design to building, testing, and deployment. It incorporates pre-built use cases and AI-driven prompts for every life-cycle stage—ranging from requirements and user stories to architecture modeling, design, code transformation and test case generation, API testing, synthetic test data and test automation.

The Gen AI Amplifier is built on a robust QE foundation using our proprietary assets, and thoroughly tested and validated to ensure consistent, reliable results. It is integrated with multiple LLMs within a secure architecture framework, which is further strengthened by our gen AI guardrails, knowledge framework, and data platforms.

The Gen AI Amplifier is helping us do in minutes and hours what software development and QA teams have traditionally needed days or weeks for:

Generating first requirements from conversation documents
Generating TMAP-guided test cases from requirements
Generating comprehensive test data
Generating automated test scripts, and more.

Generative AI adoption is driving impact across the entire test ecosystem

In the 15th World Quality Report, we learned that out of the 1,750+ senior executives surveyed globally, 64% have identified processes or applications that can benefit from AI. The majority are using AI towards building and improving test scope along with improving performance engineering, as well as the test ecosystem overall. They are actively utilizing AI to optimize their testing processes. 79% of the quality leaders agree that AI systems are going to be used to help them optimize their test scope and increase velocity.

64% of organizations have identified processes/ applications that can benefit from AI.

79% of quality leaders agree that AI systems are going to be used to help them optimize their test scope and increase velocity.

(Data from World Quality Report, 15th Edition, 2023-24)

The Gen AI Amplifier supports the full life-cycle for agile teams. We’ve integrated multiple Gen AI models in a secure environment, with pre- engineered and tested prompts built with our industry-leading quality engineering methodology, agile framework, and cloud-native architectural best practices.

With the help of our AI experts at Sogeti, the Gen AI Amplifier augments business analysts, product owners, lead architects and quality engineers at every stage – from requirements gathering and infrastructure design to comprehensive software testing.

Quality amplified early on, and at every stage, yields a significantly increased level of productivity, efficiency, and acceleration.

Closing thoughts, for now

Diving into generative AI is really exciting, and I believe in the importance of remaining grounded and focused on pragmatic experimentation. This isn’t just about incremental improvements—it’s about redefining our approach to software engineering and QA. While our ultimate goals haven’t changed, our means to achieve them have evolved. Let’s explore generative AI’s potential responsibly, learning and adapting as we go. Together, we’ll discover how these advanced tools can enhance our industry without losing sight of our core objectives.

Watch this space as we unveil more about the Gen AI Amplifier and its impact on software QA.

Author

Antoine Aymer

CTO for Quality Engineering & Testing, Sogeti

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

Testing AI Agents: A Practical Blueprint for Custom Evaluation Frameworks

May 13, 2026 by Lauren Payne

A Leader’s guide to building domain-aware evaluation disciplines that turn experimental AI pilots into production-grade, auditable enterprise systems.

The AI Agent Production Gap

AI agents are moving rapidly into software engineering, testing, DevOps, support, and back-office workflows. Many organisations have running pilots; far fewer trust those agents enough to put them into production. Gartner predicts that more than 40% of agentic AI projects may be cancelled by the end of 2027 , citing cost, unclear business value, and inadequate risk controls.

The blocker is rarely the model. It is the absence of an evaluation discipline that can answer a harder question: can this agent complete the right task, in the right context, with the right controls, consistently?

Why AI Agents Need a Different Testing Strategy

Traditional software testing assumes a known input, a fixed expected output, and repeatable behaviour. AI agents do not work that way. Outputs vary. Retrieval can return different chunks. Tool calls may take different paths. The reasoning trace shifts from one run to the next.

Evaluating an agent requires checking whether it understood the user’s intent, retrieved the right context, called the right tool, avoided hallucination, followed enterprise policy, and escalated when its confidence was low. No single generic metric covers all of this.

Existing Evaluation Frameworks Are Necessary, Not Sufficient

A strong ecosystem of evaluation tools already exists. DeepEval, Ragas, Promptfoo, LangSmith, Braintrust, TruLens, Phoenix, and OpenAI Evals each give teams real leverage on prompts, RAG pipelines, model outputs, hallucination, retrieval quality, tool calls, traces, and regression behaviour. They are essential building blocks.

But a customer-support agent, a banking-compliance agent, a test-case generation agent, and a Playwright automation agent may all share an LLM core while having entirely different definitions of “good.” Generic accuracy and faithfulness scores cannot decide whether a generated test suite is release-ready. The evaluation tool provides the engine; the organisation must define the quality model

The Custom Evaluation Blueprint

A practical custom evaluation framework can be built in seven steps:

Define the agent’s mission Write down what the agent must do, what it must never do, and how much autonomy is allowed before a human is required. This becomes the evaluation contract.
Build task-level evaluation datasets Cover normal flows, edge cases, negative scenarios, ambiguous prompts, high-risk domain cases, and historical production issues.
Create domain-specific rubrics Score domain relevance, business accuracy, retrieval correctness, reasoning, tool correctness, hallucination control, compliance, clarity, and escalation behaviour.
Apply weighted scorecards A formatting slip is low severity; a wrong business recommendation is critical; a wrong tool call may block release. Weight accordingly.
Combine automated evaluation with human calibration Automated evaluators give scale; expert reviewers calibrate the rubric over time to account for edge cases no automated scorer anticipated.
Run regression evaluation continuously Re-score whenever the model, prompt, RAG corpus, tool definition, workflow, or enterprise policy changes.
Convert scores into release gates Pass · Conditional Pass · Human Review Required · Block — each gate tied to a clear business risk threshold.

Custom Metrics Based on Business Context

Generic LLM benchmarks measure model capability in isolation. Enterprise AI agents operate in a business context — with specific user personas, data governance requirements, integration constraints, and financial consequences of failure. The metrics must reflect that context. Below is a framework for selecting and weighting evaluation dimensions by deployment domain.

Metric Clusters by Enterprise Domain

Each domain cluster below contains the metrics that carry the most signal for that type of agent. Select the cluster that matches your deployment, then tune weights using your organisation’s risk tolerance and regulatory posture.

Weighted Scorecard: Enterprise AI Agent Release Template

The table below shows how to structure a weighted scorecard across core evaluation dimensions. Adjust weights to match your domain cluster above and your organisation’s risk posture.

Business-Context Metric Matrix

The following matrix maps enterprise agent types to their primary KPI, the hardest-to-catch failure mode, and the metric that most reliably surfaces it.

Release Gates — Translating Scores into Decisions

Every weighted scorecard must terminate in a binary business decision. The four-gate model below maps score ranges to actions and assigns responsibility for each outcome.

Lessons from testron.ai Implementations

What This Means for QA Teams

Agentic AI is reshaping the QA mandate. Test execution is no longer the centre of gravity; evaluation design is. The QA function becomes the quality gatekeeper for enterprise AI agents — owning rubrics, scorecards, regression datasets, and human-in-the-loop calibration.

The skills that compound from here are domain-aware evaluation design, structured human review, and translating business risk into release gates that engineering and the business both trust.

Closing

The future of testing is not just more automation. It is trusted AI-agent evaluation: a clear mission, a custom rubric tuned to business context, a weighted scorecard, calibrated human review, and a release gate that reflects business risk.

Teams that build this discipline now will be the ones who put agents into production with confidence — and who earn the trust of the board, the regulator, and the customer.

References:
Gartner: Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 — gartner.com
DeepEval — deepeval.com

Author

Babu Manickam CTO, Indsafri

Over 27 years of experience in software testing, test automation, performance engineering, DevOps, and AI-led quality engineering. He has trained more than 50,000 QA professionals and works with enterprises to implement modern testing practices across automation, Generative AI, and agentic quality engineering. An active speaker and community contributor in the software testing ecosystem, with a strong focus on helping QA professionals transition into AI-augmented engineering roles.

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

The Real Problem with AI Testing Tools Isn’t the AI – It’s Trust

May 11, 2026 by Lauren Payne

Every software company is talking about AI. Copilots. Autonomous agents. Self-healing tests. But inside most engineering organisations, a quiet hesitation persists – not because teams doubt AI’s potential. Precisely because they do.

The real question holding teams back isn’t capability. It’s a far more fundamental one: Can we trust AI to participate in release-critical testing workflows?

That question matters because software testing is fundamentally different from most other AI-assisted tasks. A poor AI-generated image is an inconvenience. A poor AI-governed production release can damage revenue, customer trust, compliance, and operational stability.

The next evolution of quality engineering will not be driven by fully autonomous AI in isolation. It will be driven by human-controlled Agentic AI.

The Industry Has a Scaling Problem

Most organisations already have automation. What they struggle with is scaling it.

Traditional automation frameworks demand specialist skills, constant maintenance, brittle selectors, and significant engineering effort just to keep regression suites operational. As applications evolve, teams often spend more time maintaining tests than expanding meaningful coverage.

Meanwhile, release velocity continues to accelerate. Modern engineering teams are expected to deliver continuously across web, mobile, APIs, and increasingly complex customer journeys. The operational cost of maintaining automation has quietly become one of the biggest hidden blockers in software delivery.

This is where Agentic AI changes the conversation entirely.

From Scripts to Intelligent Testing Systems

At Virtuoso, our Agentic AI vision is built around a single, clear outcome:

Rather than treating test automation as a collection of static scripts, Agentic AI introduces intelligent operational workflows that help QA teams move from requirements to execution dramatically faster. The platform is designed to:

But critically, AI does not act unchecked. Every important decision point remains reviewable, traceable, and human-governed. Requirements are approved. Generated journeys are reviewed. Repairs are auditable. Changes are versioned.

The Future Is Not AI Replacing Testers

One of the most persistent misconceptions surrounding AI in testing is that the goal is complete removal of human involvement. In reality, experienced QA engineers are becoming more valuable – not less.

The organisations seeing the greatest success with AI are those combining machine scale with human judgement. That is the core philosophy behind Virtuoso’s approach to Agentic AI.

Operational QA, Not Just Better Automation

The industry has spent years focused on “test automation tools.” The next phase is operational QA systems – systems that can understand context, coordinate workflows, propose actions, and continuously improve automation at a scale that would traditionally require large engineering teams.

But operational credibility only comes from governance. Engineering leaders don’t simply need AI that can generate tests. They need AI they can trust inside release-critical pipelines. That means:

This is precisely why human-controlled Agentic AI will ultimately outperform uncontrolled autonomy in enterprise software delivery.

The Next Era of Quality Engineering

AI will absolutely transform software testing. But the winners in this space will not be organisations chasing fully autonomous systems with no oversight. The winners will be teams that successfully combine:

AI-Powered Scale
Intelligent Orchestration
Human Governance
Operational Trust

In software delivery, speed matters. But trust matters more.

Author

Andy Dickin – QA & Quality Engineering Leader Virtuoso QA

Andy specialises in AI-powered automation and modern quality engineering practices that help organisations scale software delivery with confidence.

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

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.