Early Bird Savings: 10% off Tickets

EuroSTAR Conference

Europe's Largest Software Testing Conference.

Expo

The 4 Pillars of Modern Testing: Building a Unified Ecosystem

by

In the pursuit of digital transformation, many QA teams are hindered by integration debt—the hidden cost of reconciling manual data and bridging visibility gaps between disconnected platforms. This debt is paid in the form of administrative overhead, reduced velocity, and the inherent friction that occurs when test management and test automation operate in separate silos.

To address these complexities, many organizations utilize the Inflectra ecosystem to bridge the gap between planning and execution. As a provider of software lifecycle management tools, Inflectra focuses on creating a single source of truth across the development pipeline. Within this framework, SpiraTest (test management) and Rapise (test automation) are engineered to function as a unified system, ensuring that automated execution remains tethered to original
business requirements.

1. Transitioning from Integration to Unification

Traditionally, QA has been bifurcated. Test management tools track requirements and manual progress, while automation engines function within independent environments. This disconnect creates three primary strategic risks:

  • The Transparency Gap: Stakeholders may see successful execution reports without understanding which specific business requirements have been validated.
  • Version Divergence: Automated scripts often evolve independently of the test plans they support, leading to deceptive results based on outdated logic.
  • Operational Inefficiency: Engineers frequently duplicate effort by recreating manual test steps in code because a shared source of truth does not exist.

2. Transitioning from Integration to Unification

There is a fundamental difference between two tools connected by an API and a truly unified ecosystem. In a unified environment, the automation engine is not an external add-on; it is a native extension of the management layer.

When automation is managed as a primary asset within the test management tool,
organizations achieve End-to-End Traceability. This allows a Project Manager to evaluate a high-level requirement and immediately view the precise execution logs and evidence that confirm its stability.

The Pillars of a Unified QA Workflow

To modernize a QA department, leadership should focus on four technical pillars that define a mature, unified strategy:

Pillar I: Requirement-Driven Automation
Instead of developing scripts in isolation, a unified system allows teams to derive automation directly from manual definitions. By using the manual test case as the structural blueprint, Rapise ensures that automation mirrors the original business intent. This alignment ensures that any change to a manual requirement in SpiraTest is immediately reflected in the automation gap analysis.

Pillar II: Centralized Orchestration
Automation provides the greatest ROI when it is accessible and autonomous. A unified system acts as a Command Center, enabling teams to schedule and remotely execute tests across global labs directly from the management interface. Orchestration ensures the automatic capture of screenshots, logs, and outcomes into a centralized, auditable trail.

Pillar III: Data-Driven Scalability
Professional maturity in testing involves moving beyond simple record-and-playback functions toward Parameterization and Parallel Execution. By defining complex data sets in SpiraTest and deploying them through Rapise scripts simultaneously, teams can expand test coverage exponentially without increasing their maintenance footprint.


Pillar IV: The Continuous Feedback Loop (CI/CD)
In a modern CI/CD pipeline, the test management tool serves as the definitive quality gate. When build completions trigger Rapise automations, the results feed directly into SpiraTest release dashboards. This creates a self-documenting loop that provides stakeholders with a data-backed assessment of risk before code reaches production.

Leadership Perspective: Quality as a Discipline

True software excellence happens when strategy and execution are unified. By adopting a streamlined workflow, organizations reduce toolchain complexity and eliminate the
labor-intensive effort required to synchronize disparate platforms. The result is a more resilient QA process where quality is a continuous, intelligent discipline rather than a final checkpoint.

Summary for QA Executives

If your team spends more time managing tools than testing software, your architecture is suffering from integration debt. A unified system like SpiraTest and Rapise goes beyond task automation; it synchronizes your entire quality strategy, ensuring that every automated action serves a documented business goal.

Author

Adam Sandman

Adam Sandman is a visionary entrepreneur and a respected thought leader in the enterprise software industry. As the Founder and CEO of Inflectra Corporation, Adam has dedicated his career to revolutionizing how businesses approach software development, testing, and lifecycle
management.

Under Adam’s leadership, Inflectra has become a global provider of award-winning solutions, from SpiraTest’s powerful test management and flexible automation of Rapise to SpiraTeam’s end-to-end traceability. He has led Inflectra’s suite of software to grow into a global standard that empowers teams across the world to deliver high-quality software efficiently and collaboratively. His deep technical expertise, combined with a passion for innovation, has
positioned him as a trusted voice in the field, influencing trends and shaping best practices for agile development and quality assurance.

Adam is known for his engaging presentations at industry conferences, where he shares insights on topics such as automation, project management, and emerging technologies. His ability to translate complex concepts into actionable strategies has earned him a reputation as an effective educator and mentor.

Beyond his technical acumen, Adam is committed to fostering a culture of inclusivity and collaboration within the tech community. Through thought-provoking blogs, webinars, and public speaking engagements, he inspires professionals worldwide to adopt forward-thinking approaches to software development and testing.

When he’s not at the helm of Inflectra, Adam enjoys exploring the latest advancements in technology, mentoring up-and-coming tech leaders,

Inflectra is participating in EuroSTAR Conference 2026 as a Gold Sponsor

AI is accelerating testing, but judgment still sits with people. 

by

GenAI in Testing: Acceleration Without Abdication 

In many software teams today, generative AI is becoming part of everyday engineering work. 

Within testing, it is being used to turn requirements into draft test outlines, generate realistic data variations, structure API and automation tests, refactor brittle scripts, and make sense of complex logs. What began as curiosity is now influencing how testing work is approached. 

Industry research reflects this shift. The Stack Overflow Developer Survey 2025 reports widespread use of AI tools across development workflows (https://survey.stackoverflow.co/2025/).

At the same time, the Capgemini World Quality Report highlights AI-driven testing and intelligent automation as priorities for quality leaders (https://www.capgemini.com/insights/research-library/world-quality-report/). 

AI is shaping both daily testing practice and broader quality strategy.  The important question is not whether we use these tools. It is whether we understand their limits. 

AI as Support, Not Authority 

Generative AI can suggest alternative test conditions, propose data sets, draft automation code, explain unfamiliar system behaviour, and assist with log analysis. Used carefully, it reduces friction and expands exploration. 

But it does not understand context. 

It does not evaluate risk. 

It does not decide what matters most. 

Testers do. 

AI can produce output. Testers determine whether that output addresses genuine system risk. Are edge cases meaningful? Is automation maintainable? Does the proposed coverage reflect realistic user behaviour? Has bias crept into assumptions? 

The fundamentals of testing remain unchanged: risk-based thinking, analytical depth, system modelling and critical reasoning. Working alongside AI does not replace these skills. It makes the gaps more visible when they are missing. 

Testing AI Systems Is a Different Discipline 

The shift is not only about using AI in testing. Many teams are also validating AI-enabled systems.  

These systems behave differently from traditional deterministic software. Outputs may be probabilistic. Behaviour can evolve as training data changes. Bias may appear in subtle ways. Explaining outcomes can be more complex than reproducing them. 

Testing in this environment requires additional competence. Understanding data quality, model behaviour, bias detection and the implications of non-deterministic results is increasingly part of the tester’s remit. 

The certification schemes developed by the ISTQB reflect this evolution. Specialist pathways now address AI and machine learning testing alongside established foundations in structured test design and risk analysis. 

Independent examination for these certifications is delivered globally by organizations such as iSQI GmbH. Further information on certification pathways can be found at https://www.isqi.org. 

Certification does not replace experience. It does not manufacture judgement. What it offers is structured knowledge, shared language and a clearer route for professional development in an expanding discipline. 

Learn more about Testing with Generative AI and Testing AI systems 

A Balanced View of Certification 

Certification has long been debated in the testing community. That debate is appropriate. 

Testing is contextual. It involves thinking, adapting and responding to nuance. No syllabus can capture every scenario encountered in practice. 

However, structure has value. Shared terminology reduces misunderstanding. Defined knowledge areas help organisations benchmark capability. Independent assessment provides transparency in a global profession where skills must be portable. 

The aim is not to standardise judgement. It is to support professional growth. 

Speed Is Not Insight 

AI unquestionably increases speed. Draft artefacts can be produced quickly. Data sets can be generated at scale. Logs can be summarised in seconds. 

The danger lies in mistaking output for understanding. 

Generating hundreds of test cases is easy. Determining whether they address real risk is harder. Automation can execute rapidly. Interpreting results in context still requires experience. 

When strong testing fundamentals are combined with informed AI use, productivity improves without compromising quality. When fundamentals are weak, AI amplifies the weaknesses. 

The testing profession has navigated similar shifts before. From manual execution to automation. From sequential delivery to agile development. From monolithic systems to distributed architectures. Each transition required adaptation while preserving core principles. 

Generative AI is another transition. 

The tools will continue to evolve. The need for informed human judgement will not. 

Quality has always depended on professionals who understand risk, context and consequence. That remains true. 

The real conversation is not whether AI belongs in testing. It is whether we are developing testers who can use it with discipline and intent.  

Author

Debbie Archer 

Debbie Archer is Managing Director of iSQI Limited and Vice President for Business Development at iSQI Group. With more than 20 years of experience in learning and development, she has held senior positions, including Director of Global Channel Partners at the BCS, The Chartered Institute for IT. She is actively involved with ISTQB® (International Software Testing Qualifications Board), supporting the development of globally recognised software testing qualifications. 

iSQI are a participating in EuroSTAR Conference 2026 as an Exhibitor. Join us at EuroSTAR Conference EXPO in Oslo 15-18 June 2026.

Xray App Editions: more flexibility, more choice for all teams

by

What are App Editions and why they matter

In the Atlassian ecosystem, every team has its own way of approaching testing: some are just starting out and need the basics; others are scaling automation, managing complex pipelines, or working under strict compliance standards. Until now, Xray offered a core application with the base set of features, and users could add Xray Enterprise on top of that to gain access to additional functionalities. This structure caused confusion and wasn’t taking into account users who were looking for some additional features, but not the whole Enterprise package. 

With the goal of better serving all different user needs, Atlassian introduced App Editions. Instead of publishing separate apps, vendors now have the possibility of providing a Standard version – including all the features from the original product and the Advanced version that includes a larger feature set and enhanced feature thresholds like Storage and API calls, without the significant price escalation of an Enterprise product. This model is available exclusively for Cloud apps and brings flexibility in pricing, packaging, and upgrades.

For customers, this means better-tailored options and a smoother upgrade journey adapted to your team’s unique challenges. You can select the edition that fits your team today, and upgrade to a different one, once you’re ready – without switching apps.

Xray is adopting this framework to give teams more choice, better scalability, and a simpler upgrade path.

Xray’s Editions walkthrough

Xray is moving into an app editions structure: Standard and Advanced. Each edition is designed with specific team sizes, budgets, and testing maturity levels in mind. Both will be under the same listing:

Standard edition – for smaller teams and agile starters

The Standard edition includes all the core test management features teams need to move beyond spreadsheets and manage testing directly inside Jira. It’s ideal for startups, small QA teams, or organizations looking for a cost-effective way to get structured test management in place.

It also includes Xray’s AI Test Case Generation — a feature designed to help testers write better tests faster, without replacing human expertise. Instead of immediately producing detailed tests, AI generates draft titles and descriptions based on requirements or preconditions. Testers can then review, edit, and refine them before full tests are generated, ensuring accuracy, control, and security. This feature is available in all editions.

Advanced edition – the new middle tier

The Advanced edition is built for growing QA teams that need more than the basics but aren’t ready for enterprise-scale operations.

With Advanced, you get more advanced testing features, like the additional storage (from 100Gb to 250Gb), higher API limits (from 60 RPM to 100 RPM), and additional project managing functionalities. It’s a natural next step for teams that are growing in size, expanding test automation, integrating with CI/CD pipelines, and that are running more tests on a daily basis.

For many mid-sized organizations, this will be the “sweet spot” – powerful enough to deliver immediate value, without requiring a full enterprise investment.

How you benefit from Xray App Editions

The shift to App Editions isn’t just about pricing, it’s about making Xray more adaptable to the way teams grow.

  • Flexibility and choice
    Teams can choose the edition that matches their stage of testing maturity and budget. There’s no need to overpay for features you don’t need.
  • Smooth upgrade path
    Moving from Standard to Advanced is seamless. Everything happens within the same app, so upgrades are less disruptive and don’t require migrations.
  • More value at every stage
    The introduction of the Advanced edition means mid-sized teams now have an option that fits them better.

Xray Enterprise as an independent app

With the goal of simplifying the differentiation between test management products, Xray has also released Xray Enterprise as an independent application. 

Now, instead of having to install two apps with individual pricing structures, Xray Enterprise users will have a completely separate app. This includes all Xray Standard and Xray Advanced features plus Enterprise-level feature enhancements like Test Case Designer, Test Case Versioning, Dynamic Test Plans, and Remote Jobs Trigger, as well as even higher feature thresholds, faster support SLAs, and direct contact with a dedicated account manager. Making the scaling of your QA operations smoother.

Xray Enterprise also introduces AI Test Model Generation — an exclusive capability that automatically creates visual models of your system directly from requirement content. This helps teams gain deeper insight into system behavior, identify requirement gaps early, and expand coverage confidently. Built on Sembi IQ — an AI platform for software testing and security — this feature ensures contextual, explainable, and secure intelligence, empowering teams to test faster and build safer products.

How Xray stands out in the market

Other Test Management vendors in the Atlassian ecosystem have experimented with different offerings. For example, some vendors split their offerings into separate products, sometimes even outside Jira, which can cause disruptions.

All Xray products continue to be natively integrated with Jira, whether you’re just starting with test management or you are an Enterprise-level organization with cutting-edge testing needs. With the introduction of App Editions upgrading is more accurate and less complex, without any loss of information and process changes. This consistency is what sets Xray apart: clarity, scalability, and a user experience designed to grow with your team.

Xray App Editions – FAQ

Can I upgrade from Standard to Advanced or Enterprise?

Yes. You can upgrade anytime through the Atlassian Marketplace or directly in-app. It’s instant and seamless — no data loss or migration needed.

What if both Xray Test Management for Jira and Xray Enterprise are installed?

Your instance will behave as if only Xray Enterprise is active. For best results, keep just one app installed.

I’m migrating from Data Center to Cloud — can I choose my edition?

Yes. You can move directly to Standard, Advanced, or Enterprise. The migration process hasn’t changed, and your data moves safely into the edition you select.

How do upgrades and downgrades work?

Upgrades take effect immediately. Downgrades happen at the end of your billing cycle. You can manage everything through the Marketplace or Admin Hub.

Will I lose data if I switch editions?

No. Your data remains intact — you’ll just lose access to Enterprise-only features until you upgrade again.

Does Enterprise support Data Residency?

Yes. It follows the same Data Residency regions as Xray Test Management for Jira.

What’s the difference between editions?

  • Standard: core Jira-native test management capabilities with essential reporting.
  • Advanced: everything in Standard plus smarter test design, adoption & usage insights, and expanded AI, storage, and API capacity
  • Enterprise (independent app): governance and scale for complex organizations—versioning/approvals, dynamic plans, remote job triggers, and the highest AI, storage, and API capacity.

Where can I compare prices and features?

Check the Atlassian Marketplace pages for:

With Xray App Editions, you no longer have to choose between “too little” or “too much.” Instead, you can select the edition that matches your needs today — and upgrade when you’re ready.

Whether you’re starting small or growing fast, there’s an edition designed for every part of your journey.

Xray grows with your testing.

Learn more about Xray App Editions and see which edition is right for your team.

Author

Mariana Santos, Xray, Content Marketing Manager

Mariana Santos is the Content Marketing Manager for Xray. She is responsible for implementing and promoting content that reflects the most important topics for the testing community. She focuses on highlighting crucial subjects from the software testing and quality ecosystem and communicating them continuously.

Xray are a participating in EuroSTAR Conference 2026 as a Platinum Sponsor

From Oxymoron to Reality: How to Execute Manual Tests Automatically with AI 

by

How does the automatic execution of manual tests speed up test cycles? 

On the surface, “automatic execution of manual tests” sounds like an oxymoron. The industry has always operated on a binary: manual means a human tester; automatic means a script, a framework, and an automation engineer. For decades, “manual” has been synonymous with human intervention. 

But agentic AI is turning this contradiction into a practical capability. At the heart of this shift is Lynqa, an AI agent designed to run manual tests directly on the GUI. It understands test procedures, navigates the application interface, verifies results, and delivers a detailed execution report without a single line of automation code. 

The Sprint Bottleneck: Why “Manual” Needs a Boost 

Every QA team knows the pattern: as the sprint nears completion, the volume of manual checks piles up, testers are stretched thin, and feedback to developers slows to a crawl. This is the “Manual Testing Debt.” 

The usual responses, hiring more testers or rushing to automate new features, rarely work within a single sprint. Traditional test automation simply takes too long to develop alongside the feature it’s meant to validate. 

This is where GUI AI agents change the game.  

What Are GUI AI Agents? 

GUI AI agents interact with software by visually interpreting screen interfaces and operating a virtual mouse and keyboard. Instead of relying on backend APIs, these AI agents execute complex digital workflows exactly as a human user would: 

  1. GUI-Based Actions: It interacts with the graphical interface exactly as a human would, opening browsers, scrolling through menus, filling in fields, and navigating complex clients such as ERP systems. 
  1. Visual Perception: It monitors progress by analyzing the screen at each step, identifying the areas affected by an action or verification. 

The Feedback Loop: Thought and Action 

The agent operates in a continuous feedback loop that combines reasoning with action: 

  • Interpretation: It reads the test steps and builds a logical action plan. 
  • Perception: It analyzes the current screen to decide which interaction (click, type, drag) to perform. 
  • Monitoring: After every action, it checks the UI state to verify the expected result. 
  • Communication: If the agent encounters an ambiguity or a high-risk step, it pauses to ask the user for clarification before proceeding. 

This means the agent doesn’t blindly follow a script; it reacts to the application’s actual behaviour. 

Lynqa: Agentic AI Inside Your Test Management Tool

While general-purpose AI agents are appearing in consumer chatbots, Lynqa for Jira is purpose-built for the testing ecosystem. By integrating directly into Jira/Xray, it transforms how teams handle manual workloads within a sprint. 

How it speeds up the cycle: 

  • Zero Scripting Overhead: If you have a written test case or a clearly defined User Story, the agent can execute it immediately, without waiting for an automation engineer. This enables “Day 1” testing of new features. 
  • Adaptive Execution: When a button moves, a CSS class is renamed, or a color shifts, traditional automation breaks. The AI agent sees the “Submit” button regardless of underlying code changes, removing the maintenance tax that halts progress. 
  • Analysis-Ready Reporting: The agent doesn’t just return “Pass” or “Fail.” It provides step-by-step screenshots and a detailed failure analysis, giving testers actionable information immediately. 

Start your experience with Lynqa today! We offer 10 free credits upon installation, or you can book a demo with our team. Find more information here.  

A Collaborative Future: The QA “Tribe” 

At EuroSTAR, we talk about “finding your tribe” and sharing the passion for quality. AI agents aren’t here to replace human testers; they’re here to empower the tribe. 

The most effective teams use agents like Lynqa as extra hands. While the agent handles repetitive GUI-level checks and initial failure analysis, human testers reclaim time for the work that matters most: exploratory testing, risk analysis, and strategic quality coaching for the development team. 

The tester’s role evolves from executor of steps to orchestrator of agents. As we look toward the 2026 EuroSTAR Conference, the question is no longer whether manual tests can be automated; it’s how quickly your team can leverage agentic AI to keep pace with modern delivery. 

Author

Bruno Legeard,  Smartesting

Bruno Legeard heads up the AI Lab at Smartesting (publisher of Lynqa). An expert in AI for software testing, he is one of the authors of the ISTQB CT-GenAI certification “Testing with Generative AI.” 

Meet the Lynqa team at the Smartesting booth during the EuroSTAR 2026 Conference to see agentic testing in action! 

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

Autonomous Testing with AI: Separating Hype from Real-World Value

by

Autonomous testing is one of those phrases everyone nods at, but few people can clearly explain. Slide decks talk about systems that write their own tests, fix failures automatically, and keep pipelines green without anyone watching. Somewhere along the way, teams start wondering if they are falling behind simply because they don’t have “autonomy” yet.

Most of that anxiety comes from hype, not reality. The real question isn’t whether testing can become autonomous. It’s where autonomy actually helps teams do better work, and where it quietly introduces new risks.

Autonomy is a spectrum, not a switch

Testing doesn’t suddenly flip from human-driven to autonomous. It evolves in layers.

Most teams start with automation that focuses on execution. Scripts run faster than humans, but people still decide what gets tested, how coverage is shaped, and whether results can be trusted. This is familiar territory.

AI assistance adds another layer. Test creation speeds up, failures are grouped instead of dumped into long reports and maintenance effort drops. These gains are real, but they are still reactive. Humans remain firmly in control.

Autonomy begins only when a system can take on limited decision-making, within boundaries defined by the team. Not creative judgment, but practical judgment. Deciding which tests are relevant for a specific change. Flagging failures that look like noise rather than regressions. Noticing flows that are becoming so fragile they can no longer be trusted. This is usually where expectations and reality part ways. What is sold as autonomy often turns out to be faster automation with a new label.

Why Hype Breaks Down in Real Environments?

Autonomous testing struggles when context is missing.

AI does not understand business importance on its own. It cannot tell which workflow carries regulatory exposure or which release is under executive scrutiny. Without that context, decisions become statistical guesses rather than informed choices.

Self-healing is another frequent weak spot. Updating locators can keep tests running, but it can also hide changes in behavior that actually matter. Pipelines look stable while confidence quietly erodes.

A green pipeline is comforting. A trustworthy pipeline is far more valuable. Autonomy that masks risk does more harm than good.

Where AI Genuinely Helps?

Used well, AI shines at recognizing patterns humans don’t have time to see.

It can connect failure signatures across pipelines and environments. It can reveal tests that only break under specific data conditions. It can point out entire suites that consume time without improving coverage. It can highlight areas where manual testing consistently finds issues automation misses.

This doesn’t replace testers. It sharpens their judgment.

One of the most underrated benefits of AI-assisted testing is how it changes team discussions. Less time is spent arguing about flaky failures. More time is spent talking about system behavior and risk. For many teams, that shift alone justifies the investment.

Autonomy Needs Boundaries to be Trusted

The most effective autonomous testing systems are deliberately limited.

They don’t decide what quality means; teams do.

They don’t invent test strategies. They optimize what already exists.

They don’t operate silently. They explain their reasoning.

Explainability is non-negotiable. If a system skips tests or classifies failures without showing why, teams will override it every time. Accuracy matters, but transparency is what builds trust.

Autonomy is adopted when people understand not just the outcome, but the reasoning behind it.

From Scripts to Systems

Traditional automation treats tests as isolated scripts. Autonomous testing treats them as parts of a larger system.

That system understands dependencies between services, data, environments, and user flows. It recognizes that a login failure ripples across dozens of downstream tests. It understands that a configuration change in one region shouldn’t invalidate results everywhere else.

This shift is subtle, but important. Autonomy works not because AI is smarter than humans, but because it can track complexity humans can’t reasonably keep in their heads.

This way of thinking is increasingly reflected in how enterprise platforms are being designed. At ACCELQ, autonomy is treated less as an end state and more as a support system for decision-making at scale. The emphasis is on observing system behavior, correlating signals across pipelines, and making change easier to understand rather than hiding it behind execution.

Capabilities such as adaptive test generation, intelligent handling of change, and agent-style execution through ACCELQ Autopilot are used to reduce noise and maintenance while keeping teams firmly in control of intent and strategy. Autonomy, in this model, is not about removing oversight. It is about making complex testing environments more transparent as systems evolve.

How Teams Should Evaluate Autonomy Today?

A simple way to cut through autonomous testing claims is to ask a few uncomfortable questions. What decisions does the system make without human input? What signals drive those decisions? How does it behave when signals conflict? And how easy is it to override decisions and learn from them?

Vague answers usually point to cosmetic autonomy.

Real autonomy shows up quietly. Fewer late-night reruns. Fewer ignored failures. Fewer production surprises. It reduces friction without asking for attention.

The Future is Assistive, Not Absent

The strongest testing organizations aren’t trying to remove human judgment. They are trying to protect it.

AI is well-suited to repetition, correlation, and scale. Humans are better at intent, ethics, and trade-offs. Autonomous testing works when it creates space for thinking, not when it pretends thinking is no longer needed.

The real value isn’t in replacing testers. It’s in freeing them from work that hides risk instead of revealing it.

That’s the point where autonomy stops being a promise and starts being useful.

Author

Geosley Andrades, Senior Director,  ACCELQ

Geosley is a Senior Director, Product Evangelist and Community Builder at ACCELQ, leading global AI-driven, no-code test automation initiatives alongside product strategy and go-to-market programs. With nearly 18 years of cross-industry experience, he helps enterprises rethink how software quality is built, validated, and scaled for real-world impact. A strong advocate for intelligent, autonomous testing at enterprise scale, Geosley actively shapes ACCELQ’s vision through competitive analysis, analyst engagement, and forward-looking research—driving simpler, more reliable, and sustainable automation for modern digital ecosystems.

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

How Agentic AI is Transforming Software Delivery Lifecycles 

by

Software delivery has always been a balancing act between speed, quality, and risk. As enterprises adopt cloud-native architectures, DevOps, and continuous delivery models, that balance is becoming harder to maintain. Traditional automation and AI tools help — but they still rely heavily on human direction. 

Agentic AI introduces goal-driven intelligence into the software delivery lifecycle (SDLC), enabling systems to adapt, recommend, and act within defined enterprise guardrails. Rather than replacing human decision-making, agentic AI augments teams with continuous intelligence and policy-aware automation. The result is faster releases, higher quality software, and delivery pipelines that continuously optimize themselves. 

In this blog, we explore what agentic AI is, how it applies to software delivery, and why it represents the next evolution of enterprise DevOps. 

What is Agentic AI in Software Delivery? 

Agentic AI refers to artificial intelligence systems designed to act autonomously in pursuit of defined goals, rather than simply responding to prompts or executing predefined rules. 

In the context of software delivery, agentic AI systems can 

  •  Analyze delivery data across tools and teams. 
  • Make context-aware recommendations and trigger actions with minimal manual intervention, while remaining aligned to enterprise policies and approval models. 
  • Take coordinated action across the SDLC (planning, testing, release, operations) by leveraging a unified platform that connects delivery data, workflows, and governance in one system of record.
  • Learn and adapt based on outcomes. 

Unlike traditional AI, which primarily assists humans with insights or recommendations, agentic AI acts as an intelligent participant in the delivery process. 

The Role of Agentic AI Across The Software Delivery Lifecycle 

Agentic AI doesn’t replace DevOps teams — it augments them by operating continuously across every phase of delivery. 

Planning and Prioritization 

Agentic AI can analyze backlogs, historical delivery data, and business objectives to recommend optimal sprint scope, identify high-risk dependencies, and dynamically reprioritize work based on changing conditions. Instead of static planning cycles, teams gain adaptive planning intelligence that evolves in real time. 

Development and Build Automation 

During development, agentic AI agents can 

  • Detect code patterns linked to future defects. 
  • Optimize build pipelines based on performance trends. 
  • Flag architectural risks earlier in the process. 

This proactive intelligence reduces downstream rework and accelerates time to value. 

Continuous Testing and Quality Management 

Testing is where agentic AI delivers some of its greatest impact. AI agents can can intelligently prioritize and optimize test execution based on code changes, historical risk patterns, and release context—reducing redundancy while increasing confidence in release readiness. This leads to faster feedback loops and higher confidence in release readiness. 

Deployment and Release Optimization 

Agentic AI enables smarter, safer deployments by selecting optimal deployment windows, monitoring live performance and user impact, and detecting performance or reliability thresholds in real time and initiating guided remediation workflows. The result is a more resilient release process with fewer disruptions. 

Post-Release Learning and Optimization 

Unlike traditional automation, agentic AI continues learning after release by analyzing: 

  •  Customer feedback 
  • Production incidents
  • Delivery performance metrics

Those insights feed back into planning and execution, creating a continuously optimizing delivery lifecycle—where insights from production, quality, and delivery performance feed back into planning and execution with full transparency. 

Why Agentic AI Matters For Enterprise DevOps Teams 

Enterprise DevOps environments are complex — multiple tools, distributed teams, hybrid architectures, and competing priorities. Research from Google’s DORA team has consistently shown that high-performing DevOps organizations deploy software more frequently, recover faster from incidents, and maintain higher reliability than their peers. As delivery complexity increases, agentic AI helps teams manage that complexity at scale by enabling autonomous decision-making across the software delivery lifecycle. 

Google’s DORA research shows that high performance in deployment frequency, lead time, failure rate, and restoration time strongly correlates with effective DevOps strategies. 

Key benefits include: 

  •  Faster delivery cycles through autonomous decision-making 
  • Improved quality with predictive defect detection 
  • Reduced operational risk via real-time monitoring and response 
  • Lower cognitive load on teams, freeing humans to focus on innovation 

Rather than replacing human expertise, agentic AI allows teams to operate at a higher strategic level. 

How Agentic AI Fits into The Modern Software Delivery Stack 

Agentic AI works best when embedded within an integrated, enterprise-grade software delivery platform that connects planning, development, testing, and operations data. 

A unified platform enables AI agents to see the full delivery value stream, correlate signals across tools, and take informed, context-aware actions. This is where modern, AI-enabled delivery platforms play a critical role. 

Real-World Use Cases of Agentic AI in Software Delivery 

Organizations adopting agentic AI are already seeing tangible results, including: 

  • Automatically identifying release risks before production 
  • Reducing test execution time while improving coverage
  • Accelerating recovery from incidents without manual intervention
  • Optimizing delivery flow across large, distributed teams  

These outcomes are especially valuable for enterprises managing mission-critical applications and frequent releases. 

The Future of Software Delivery is Autonomous and Intelligent 

As software delivery continues to accelerate, static automation and manual oversight will no longer be enough. Agentic AI represents the next step forward — enabling delivery systems that think, act, and improve continuously. 

For organizations looking to improve speed, quality, and resilience at scale, agentic AI isn’t a future concept — it’s a competitive necessity. 

By combining agentic AI with an integrated software delivery platform, enterprises can unlock a new level of delivery performance and innovation. 

Deliver Software Faster—With Intelligence Built in 

OpenText brings agentic AI to software delivery by connecting planning, development, testing, and operations into a single intelligent platform. See how autonomous insights and actions can help your teams deliver higher-quality software at speed. 

For more information about the OpenText Core Software Delivery Platform, Visit us at EuroSTAR 2026 on Stand 5 or click here. 

Author

Gabriel Martinez, Director of DevOps Product Marketing, OpenText 

Gabe’s enthusiasm for tech is only matched by his passion for marketing it. With a 15-year sprint through DevOps, Cloud Management, and Application Security, he’s played a key role at Broadcom/VMware, CloudBees, Electric Cloud, and CA Technologies. Currently, Gabe is the Director of Product Marketing for ADM and DevOps at OpenText, leveraging storytelling and strategy with creativity and data-driven precision to orchestrate growth and innovation. 

OpenText are Exhibitors in this years’ EuroSTAR Conference EXPO. Join us in Oslo 15-18 June 2026.

Oslo, Norway Travel Tips: Where to Stay, Eat & Explore 

by

EuroSTAR 2026 is heading north and we’re bringing the magic of Europe’s biggest testing EXPO to stunning Norway! From 15–18 June, we’ll take over Nova Spektrum in Lillestrøm, just minutes from vibrant Oslo.  

Whether you’re joining us to learn, network, or showcase your tools, make the most of your trip with this quick guide on where to stay, what to eat, and how to explore Oslo while you’re here.

Where to Stay – Stay Close or Soak Up the City 

  • Steps from the Venue: Book a hotel in Lillestrøm for the easiest mornings – just a short stroll to Nova Spektrum and back to your room after a busy day. 
  • City Adventure: Stay in downtown Oslo to enjoy the buzz of the capital. With trains running frequently, you’ll be at the venue in 12 minutes, plus you’ll have nightlife, shopping, and cultural sights on your doorstep. 

💡 Tip: June is peak season in Norway – secure your hotel early for the best rates and availability.

Where to Eat – Flavours You’ll Love in Oslo 

Norway’s food scene is fresh, simple, and full of flavour. During your stay, treat yourself to: 

  • Taste Tradition: Smoked salmon, shrimp sandwiches, Norwegian meatballs, and the famous sweet Brunost (brown cheese). 
  • Dine by the Fjord: Enjoy fresh seafood and stunning views at waterfront restaurants – perfect for a relaxing dinner or team night out. 
  • Foodie Hotspots: Head to Grünerløkka for artisan coffee, cool cafés, and street food vibes, or visit Mathallen Food Hall to sample Norwegian cheeses, cured meats, and global street eats under one roof. 

💡 Tip: Oslo’s restaurants are popular in summer – book your evening meals early to guarantee a table. 

Explore Oslo – Adventure Beyond the EXPO 

If you’re arriving early or staying longer, take time to experience the best of Oslo: 

  • Oslo Fjord Cruises: Relax on a boat ride and take in panoramic views of the city and islands. 
  • Oslo Opera House: Walk on its iconic sloped roof for unbeatable sunsets and cityscapes. 
  • Vigeland Sculpture Park: Stroll through this vast outdoor gallery featuring 200+ sculptures. 
  • Viking Ship Museum: Step back in time and see beautifully preserved Viking ships and artifacts. 

💡 Tip: Oslo is compact and easy to explore. Grab a day pass for unlimited rides on trams, buses, and ferries, an easy way to see it all. 

Weather in June – Sunshine with a Nordic Breeze

 June is the perfect month to visit Norway. Expect long, bright days with temperatures around 19–20°C and cooler evenings near 9°C. Light rain is possible, so pack a lightweight jacket and comfy layers to stay prepared. 

Quick Essentials – Know Before You Go 

  • 🗓️ Dates: 15–18 June 2026 
  • 🚆 Transport: 12-minute train ride from Oslo Central Station 
  • 💬 Language: English is widely spoken 
  • 💰 Currency: Norwegian krone (NOK) 
  • 🎒 Packing: Layers + light rain jacket + comfortable shoes for exploring 

Pack your bags, bring your curiosity, and join us for four unforgettable days in Norway of learning, networking, and showcasing the future of testing. Let’s make EuroSTAR 2026 your most impactful event yet.

👉 Book your stand today!

Author

Clare Burke

EXPO Team, EuroSTAR Conferences
With years of experience and a passion for all things EuroSTAR, Clare has been a driving force behind the success of our EXPO. She’s the wizard behind the EXPO scenes, connecting with exhibitors, soaking up the latest trends, and forging relationships that make the EuroSTAR EXPO a vibrant hub of knowledge and innovation.


t: +353 91 416 001
e: clare@eurostarconferences.com

Are You Really Agile? Self-Asses If Your QA Team Is Truly Agile

by

Adopting Agile practices doesn’t automatically mean your QA team is working in an Agile way. Many teams follow the structure of Agile like sprints and retrospectives, but still struggle to fully embrace Agile principles. True agility in QA is about flexibility, continuous improvement, and deep collaboration to ensure quality at every stage of development.

Many teams find themselves wondering: Are we really Agile, or just following a checklist of Agile rituals? That’s the exact question explored in our ebook, “Are You Really Agile? A Practical Guide for QA Teams.”

In this article, we’ll explore key insights from the ebook to help you assess your QA team’s Agile maturity and share practical strategies to strengthen your processes.

Signs That You’re Not Truly Agile

These are the most common indicators that suggest your team is not Agile:

1.Inflexible Processes
Agile is meant to be iterative, yet some QA teams still rely on rigid, step-by-step workflows that don’t leave room for adaptation. If your testing approach isn’t flexible enough to accommodate changing requirements, it may be limiting your agility.

2.Communication Silos
Agile emphasizes ongoing collaboration, but if QA and development teams rarely interact outside of sprint reviews or retrospectives, valuable discussions may be missed. Continuous alignment is key to delivering high-quality software efficiently.

3.Lack of Continuous Feedback
In Agile, testing and feedback should happen throughout the sprint, not just toward the end. If your team is catching defects late in the cycle rather than identifying issues early, your process might be more reactive than proactive.

4.Testing as a Separate Phase
Testing should be seamlessly integrated into development, not treated as a final step before release. If QA still operates as a standalone phase rather than being part of the sprint’s workflow, it’s a sign that your team hasn’t fully embraced Agile testing.

Take This Self-Assessment to Know If You Are Truly Agile!

To help you evaluate how Agile your QA processes really are, we’ve designed a straightforward self-assessment questionnaire. It allows you to analyze your workflows, collaboration, and testing practices to see where your team stands on the Agile spectrum.

How it works: Rate your team on a scale from 1 to 5, with 1 meaning “Never” and 5 meaning “Always.”

CategoryAssessment QuestionScore (1-5)
Flexibility in ProcessesDo your QA and development processes allow frequent changes?
Are test cases and schedules adaptable as features change?
Early Involvement of QA (Shift-Left Testing)Is QA involved from the beginning, refining user stories?
Are test cases planned in parallel with feature development?
Collaboration Between QA and DevelopersDo QA and developers work closely together during sprints?
Is there ongoing communication between QA and developers?
Continuous Feedback LoopsAre feedback loops frequent during the sprint?
Is feedback (and bugs) from the testing team assessed and addressed quickly?
Automation in TestingDo you generate automation for the stories being developed during the sprint (vs automated on later sprints)?
Are automated tests run for each significant code change?
Test Case Reusability and MaintenanceIs your test library modular and easy to maintain?
Are automated tests regularly updated?
Defect Management and PrioritizationAre defects prioritized and resolved within the sprint?
Are there clear criteria for classifying defects?
Continuous Improvement and RetrospectivesAre QA processes included in sprint retrospectives?
Are metrics used to drive continuous improvement?

After completing the self-assessment, review your total score to understand where your team stands in Agile maturity:

  • 35 – 40: Your QA team is highly Agile, effectively embracing flexibility, collaboration, and continuous improvement.
  • 25 – 34: You’re heading in the right direction, but there’s room to improve. Identify lower-scoring areas and apply the strategies in this guide to strengthen your Agile approach.
  • 15 – 24: While some Agile practices are in place, there are noticeable gaps in your QA processes. It’s time to rethink your approach to collaboration, feedback loops, and integrating testing throughout the sprint.
  • Below 15: Your team may be Agile in name only. Consider revisiting Agile fundamentals and restructuring your QA processes to align with core Agile principles.

3 Key Strategies to Maximize QA Efficiency in Agile

  1. Shift-Left Testing

A core Agile principle is embedding QA early in the development process. However, many teams still follow outdated habits, treating testing as an afterthought rather than an integral part of the sprint. This delay often results in defects being discovered late, leading to costly rework, missed deadlines, and misaligned expectations.

Best Practice: Establish a continuous feedback loop between product owners, developers, and QA from the initial requirement discussions. Ensuring clear acceptance criteria and well-defined testable requirements helps prevent last-minute surprises.

Practical Tip 1: Use tools like mind maps or flow diagrams to visualize user journeys, dependencies, and potential risks during requirement gathering. This helps teams proactively identify edge cases and improve test coverage.

Practical Tip 2: Implement Behavior-Driven Development (BDD) to foster collaboration between QA, developers, and product owners. Writing test scenarios in plain language ensures shared understanding and helps translate requirements directly into test cases.

  1. Defect Management & Resolution

In Agile, defects should be handled as they arise, not postponed to future sprints. Without a structured defect management process, teams risk becoming overwhelmed, delaying essential fixes, or failing to address critical issues in time. Effective defect management is all about prioritization. Not every defect requires immediate attention, so it’s important to classify and address issues based on their impact.

Best Practice: Hold regular triage meetings to review, prioritize, and assign defects. This process ensures that the most critical issues are addressed first while maintaining transparency around defect resolution. A well-defined triage system keeps teams focused on resolving blockers before handling lower-priority fixes.

Practical Tip: Use a defect-tracking tool to maintain full visibility into defect status and ownership. Set up automated notifications for high-priority issues to ensure they are addressed immediately and don’t get lost in the backlog.

  1. Post-Sprint Retrospectives

A sprint’s conclusion is a great chance to reflect, improve, and refine processes. Retrospectives play a key role in Agile, offering teams the opportunity to analyze what worked well, what didn’t, and how to enhance efficiency in future sprints. QA is often overlooked in retrospectives, with discussions focusing primarily on development progress and sprint goals. But reviewing testing metrics such as defect resolution times, test coverage, and testing bottlenecks, can provide valuable insights into areas that need improvement.

Best Practice: Make testing a core part of sprint retrospectives. Review key QA metrics, including defect trends, risk assessment, and testing efficiency. Encourage all team members to share their insights on refining QA processes for the next sprint.

Practical Tip: Track and analyze sprint metrics to drive improvements. If defect resolution times were longer than expected, identify the root cause and adjust workflows. If test coverage was insufficient, explore ways to improve automation or optimize manual testing to focus on critical areas.

Take Your Agile QA to the Next Level

In this article, we outlined some key Agile strategies like Shift-Left Testing, Defect Management and Resolution, and Post-Sprint Retrospectives, which are essential for QA teams looking to optimize efficiency and improve collaboration. But these are just a few ways..

In the complete ebook, “Are You Really Agile? A Practical Guide for QA Teams,” you’ll find even more actionable insights and strategies designed to maximize QA impact in an Agile environment. From test execution to reporting, this guide is filled with practical recommendations to help you align your QA processes with true Agile principles.

Author

PractiTest exhibitors at EuroSTAR

PractiTest

PractiTest is an end-to-end SaaS test management platform that centralizes all your QA work, processes, teams, and tools into one platform to bridge silos, unify communication, and enable one source of truth across your organization.

With PractiTest you can make informed data-driven decisions based on end-to-end visibility provided by customizable reports, real-time dashboards, and dynamic filter views. Improve team productivity; reuse testing elements to eliminate repetitive tasks, plan work based on AI-generated insights, and enable your team to focus on what really matters.

PractiTest helps you align your testing operation with business goals, and deliver better products faster.



PractiTest WERE Exhibitors in EuroSTAR 2025. Join us at EuroSTAR Conference in Oslo 15-18 June 2026.

part of the