Elevate Your Testing Expertise with Practical Training Programs

Our training sessions are designed in collaboration with industry experts and thought leaders, offering you valuable insights, proven methodologies, practical tips, and hands-on expertise drawn from real-world scenarios. Enhance your automation testing capabilities and stay competitive in the fast-changing technology landscape. Sign up for FREE to attend the webinar of your choice and take your skills to the next level.

Webinar 1: AI Strategy for a Testing Organization

Objective of the Webinar – BlinqIO

The primary goal of this webinar was to demonstrate how AI, particularly generative AI, can transform software testing by

• Reducing manual effort and maintenance costs

• Speeding up release cycles

• Increasing test coverage and accuracy

Presenters

• Tal: A testing industry veteran with 25 years of experience. Discussed strategic aspects of adopting AI in testing.

• Sapneesh (Savannah): Technical Lead at Blinq IO. Demonstrated a live AI-driven test automation scenario.

• Joe: (Moderator)

Brief Summary of the Webinar

• Introduction: Explained the challenges in modern testing (fast release cycles, complex environments, and high maintenance costs).

• Generative AI Overview: Showed how AI agents can autonomously create, execute, and maintain test scripts.

• Live Demo: Showed Blinq IO’s AI Recorder generating Playwright code from user actions, handling changes in the UI automatically, and integrating with CI/CD pipelines.

• Strategic Insights: Emphasized the need for a “thinker/owner” AI model an autonomous approach that learns continuously and reduces manual interventions.

• Organizational Impact: Described how roles evolve from hands-on testers to supervisors of AI, with new opportunities for upskilling.

Features and Technical Aspects Discussed

• AI Test Creation

  • Automatic generation of test scenarios and scripts from user actions.

  • Zero or minimal coding required by manual testers.

• Self-Healing / Maintenance

  • AI autonomously updates scripts when the UI changes.

  • Reduces human intervention and debugging effort.

• Data-Driven Testing & Negative Testing

  • Allows integration with CSV, databases, or APIs.

  • Potential for random data generation and negative test scenarios.

• CI/CD Integration & Parallel Execution

  • Supports popular pipelines (GitHub Actions, Jenkins, etc.).

  • Enables large-scale parallel test execution for faster feedback.

• Analytics & Root-Cause Analysis

  • AI automatically classifies failures (e.g., network, DevOps, code bugs).

  • Logs or JIRA tickets can be auto-generated with relevant details.

• End-to-End Testing

  • AI can handle complex end-to-end UI testing across multiple platforms, languages, and devices.

• API Testing

  • AI can also automate API testing (though not demonstrated in the webinar).

Main Advantages & Differentiators

• High Coverage & Accuracy AI-driven tests run consistently without fatigue.

• Faster Release Cycles Automation setup and self-healing drastically shorten test phases.

• Reduced Maintenance Costs AI handles ongoing updates, freeing testers from repetitive tasks.

• Scalability & Adaptability Easy to onboard more AI agents for larger or more complex projects.

• Open Source Code Generation Uses Playwright (JavaScript/TypeScript), mitigating vendor lock-in.

How GoTestPro Can Compete

GoTestPro offers a similar AI-driven approach. To remain competitive or surpass the solution demonstrated in the webinar

• Emphasize Comprehensive Self-Healing Match or exceed Blinq IO’s autonomous script maintenance.

• Robust CI/CD Integration Offer seamless pipelines, parallel runs, and multi-platform support.

• User-Friendly Recorder Ensure that non-technical testers can create and manage tests easily.

• Advanced Data/Negative Testing Provide extensive coverage for complex scenarios (financial, healthcare, etc.).

• Scalable & Flexible Architecture Allow customers to retain ownership of generated code and adapt the solution in their environment.

Additional Observations

• Customization & Code Ownership BlinqIO’s approach demonstrates that fully generated Playwright code can be viewed, edited, and version-controlled.

• Cloud vs. On-Premises Potential concerns around data privacy and vendor stability can be mitigated by ensuring customers have full access to code and optional on-premises deployment.

• Organizational Change Management Adopting AI requires transparent communication about new roles and upskilling opportunities, reducing resistance from manual testers.

Webinar 2: From Manual QA to Automation Engineer Using AI

Company

Blinq IO Demonstrated an AI-driven test automation platform.

Objective of the Webinar

The primary goal was to illustrate how Generative AI can empower manual testers to quickly transition into automation engineers. The presenters demonstrated

• How AI can record manual testing flows and produce maintainable, open-source code.

• Ways AI handles UI changes (self-healing) and integrates with CI/CD pipelines.

• The strategic value of reducing manual effort, accelerating release cycles, and improving ROI.

Presenter of the Webinar

• Tal (20+ years in software testing, previously co-founded a test automation platform.)

• Sapneesh (Head of QA at Blinq IO)

• Joe (Moderator)

Brief Summary of the Webinar

• Introduction to AI in Testing

  • Described modern testing challenges: complex UIs, frequent releases, high maintenance costs.

  • Emphasized that Generative AI can create and maintain test automation code, reducing manual workload.

• Live Demo (BlinqIO)

  • Demonstrated how a manual tester’s clicks are automatically converted into code (Playwright).

  • Showed “self-healing” capabilities, where AI updates scripts when the UI changes.

  • Highlighted integration with CI/CD tools for continuous testing.

• Benefits for Manual Testers

  • No coding expertise required to produce or maintain test scripts.

  • Ability to add assertions, handle multi-factor authentication, random test data, and more.

• Q&A

  • Addressed data privacy, performance testing, advanced configurations, and synergy with existing tools like JIRA.

Features and Technical Aspects Discussed

• AI-Driven Recording

  • Captures user flows in real-time, generating business-level descriptions and code (Playwright, JavaScript/TypeScript).

• Self-Healing / Maintenance

  • AI autonomously updates scripts when UIs or application flows change.

• Data-Driven Testing

  • Integrates with CSVs, APIs, or random data generation for robust test coverage.

• CI/CD Integration

  • Works with GitHub Actions, Jenkins, Azure DevOps, etc., enabling automated nightly or on-demand runs.

• Analytics and Reporting

  • Provides detailed screenshots, logs, and root-cause analysis for failed tests.

• Open-Source Code

  • Users retain full access to generated Playwright code, minimizing vendor lock-in.

• Multilingual and Multi-Platform Support:

  • AI can generate test automation code for web, mobile (iOS and Android), and multilingual applications.

• Parallel Execution:

  • Tests can be executed in parallel across multiple environments.

• BDD (Behavior-Driven Development):

  • AI generates test scenarios in a structured BDD format.

Main Advantages and Differentiators

• Rapid Transition for Manual Testers

  • Manual testers can produce enterprise-grade test automation without deep coding knowledge.

• Reduced Maintenance Effort

  • AI continuously adjusts scripts to UI changes, minimizing manual debugging.

• Scalable and Flexible

  • Handles parallel executions, different browsers, and multiple environments.

• Detailed Reporting and Logs

  • Facilitates quick root-cause analysis and defect triaging.

• Ownership of Code

  • Full code visibility in Playwright, allowing customization and advanced usage.

How GoTestPro Can Compete

Given that GoTestPro offers a similar AI-driven approach, it can

• Highlight Self-Healing Strengths

  • Demonstrate robust maintenance capabilities and advanced UI change handling.

• Focus on Usability

  • Provide a user-friendly interface for non-technical testers.

• Leverage CI/CD Integration

  • Showcase seamless pipelines and quick feedback loops.

• Open-Source or Flexible Codebase

  • Emphasize code ownership and minimal vendor lock-in.

• Address Enterprise Needs

  • Offer on-premises or private cloud deployments, ensuring data privacy and compliance.

Webinar 3: The Journey to Becoming a Test Superhero

The conversation frequently mentions various AI-driven testing solutions. several tools and approaches were discussed

• Checki / http://Testers.ai AI-based testing approach mentioned as an example.

• Eggplant AI (KeySight) Discussed as a visual automation tool with AI capabilities.

• Cursor, DevN, Codeium, Anthropic, OpenAI – Mentioned as broader AI and code-generation platforms relevant to testing.

Objective of the Webinar

This webinar aimed to explore how Generative AI and Agentic AI are reshaping the software testing landscape. The main focus was on:

• Transitioning from manual or traditional automation to AI-first approaches.

• Understanding the new roles and opportunities for testers in an AI-driven future.

• Highlighting the practical steps testers can take now to prepare for the shift.

Presenters

• Jonathan Discussed enterprise-grade AI solutions, large action models (LAM), and the shift toward agentic AI.

• Jason Shared experiences with AI-driven test automation (e.g., Checki / http://Testers.ai ), including how AI can autonomously generate tests.

• Joe (Host) Moderated the session, asked clarifying questions, and guided the conversation.

Brief Summary of the Webinar

Overview of Agentic AI The panel explained that AI is evolving from simple code generation to agentic systems capable of autonomously performing tasks like running tests, identifying bugs, and even adapting to new UIs.

• Implications for Testers

  • Many current testing practices (e.g., writing Selenium scripts) may become obsolete.

  • Skilled testers will shift to critical thinking, domain expertise, and oversight of AI agents.

• Real-World Examples:

  • Anthropic’s “Computer Use” feature demonstrates how AI can control the desktop environment.

  • Tools like Eggplant AI and Checki/Testers.ai highlight end-to-end testing with minimal human intervention in coding.

Features and Technical Aspects Discussed

• Agentic AI / Large Action Models (LAMs)

  • AI that not only generates code but also executes and adapts to tasks without explicit user prompts.

  • Potential to control desktop apps, web apps, and entire workflows.

• Self-Healing / Automatic Maintenance

  • AI can autonomously update tests when UIs or features change.

• Visual vs. DOM-based Testing

  • Tools like Eggplant AI use image recognition; other frameworks rely on DOM selectors.

  • AI can unify these approaches, reducing reliance on brittle locators.

• Data Privacy and Infrastructure

  • Enterprises may need on-prem or private cloud solutions for compliance (e.g., EU AI Act).

• Developer-Driven vs. Tester-Driven AI

  • Developers are increasingly using AI to generate and test code quickly.

  • Testers must leverage AI to avoid being outpaced by Dev-oriented solutions.

Main Advantages and Differentiators

• Massive Productivity Gains

  • AI can do “grunt work” (e.g., repetitive regression tests) at scale.

• Enhanced Test Coverage & Speed

  • Parallel AI agents can explore multiple scenarios in minutes.

• Future-Proof Skills

  • Testers focusing on domain knowledge, analysis, and oversight will remain crucial.

• Adaptability

  • Agentic AI can shift between different platforms (mobile, web, desktop) with minimal reconfiguration.

Future Job Specifications

• AI Test Wranglers / Overseers Manage AI-driven testing processes, interpret complex results, ensure compliance.

• Subject Matter Experts (SMEs) Provide domain insight and critical thinking to validate AI findings.

• Multi-Disciplinary Collaboration Testers will collaborate closely with Dev, Ops, and Security, all using AI tools.

How GoTestPro Can Compete

Given the emerging dominance of AI-first testing, GoTestPro can

• Offer AI-Driven Test Generation and Maintenance

  • Implement generative AI to reduce scripting and maintain tests autonomously.

• Provide Agentic Workflows

  • Develop or integrate with agentic AI frameworks, enabling end-to-end automation across platforms.

• Prioritize Enterprise-Grade Security

  • Offer on-prem or private cloud solutions to address compliance and data privacy.

• Integrate Domain Expertise

  • Focus on user-friendly dashboards, allowing SMEs to guide AI without heavy coding.

Webinar 4: Maximizing the Value of Performance Testing (SDLC)

Combining Proven Approaches with AI

Aboud Company/Product

Company Forte Group

Presenters Role Lee Barnes, Chief Quality Officer at Forte Group

Referenced Tools & Technologies Various performance testing and APM/observability tools (Dynatrace, AppDynamics, DataDog, browser-level scripting, protocol-level scripting).

Objective of the Webinar

The main goal was to demonstrate

• How to incorporate performance testing throughout the software delivery lifecycle (SDLC).

• Best practices to ensure performance tests yield actionable findings (rather than “check the box” testing).

• Ways AI can assist in planning (behavior modeling) and analysis (anomaly detection, correlation) to boost testing efficiency and effectiveness.

Brief Summary of the Webinar

• Value of Performance Testing

  • True value lies in findings and actionable insights, not just running tests.

  • Good tests avoid catastrophic failures (cost avoidance) and optimize resource usage (cost optimization).

• Common Pitfalls

  • Check-the-box testing: Vague objectives, last-minute tests, and misleading results.

  • Lack of context in reporting leads to poor decisions.

• Best Practices

  • Plan Define measurable, clear performance objectives early.

  • Develop Choose scripting approaches (protocol vs. browser), parameterize data, ensure environment readiness.

• Execute & Analyze

  • Monitor system metrics, correlate them to end-user response times, and produce valuable, context-rich findings.

• Continuous Performance Testing

  • Integrate smaller, quicker performance checks throughout the SDLC (commit, integration, staging).

  • Use trending and baseline comparisons for each build to catch regressions early.

  • Maintain robust APM/observability tools for deeper insights.

• AI in Performance Testing

  • Planning Phase AI-based user behavior modeling (from historical logs) to define realistic load profiles.

  • Analysis Phase Machine learning can detect anomalies, correlate metrics faster, and reduce manual investigative effort

Features and Technical Aspects Discussed

• Traditional vs. Continuous Performance Testing

  • Old “Big Bang” approach vs. iterative, pipeline-driven tests.

• Protocol-Level vs. Browser-Level Scripting

  • Trade-offs: protocol is resource-efficient, browser is more realistic but heavier on infrastructure.

• Observability & APM Integration

  • Essential for identifying and correlating root causes, especially at scale.

• AI-Driven Capabilities

  • Anomaly detection, root cause suggestion, behavior modeling, correlation analysis.

Main Advantages and Differentiators

• Proactive Issue Detection

  • Shift left approach lowers mitigation costs, avoids late-cycle surprises.

• Efficient Use of AI

  • AI helps interpret vast telemetry data, saving time and uncovering hidden issues.

• Scalable Practices

  • Balances resource usage with thorough coverage via protocol or browser-level scripts.

• Context-Rich Reporting

  • Ensures stakeholders (technical & business) understand performance risks and objectives.

Job Specifications in the Future

• Performance testers increasingly need AI familiarity to leverage advanced APM capabilities.

• Collaboration with Dev, Ops, and business stakeholders becomes critical.

• Skills in continuous performance testing and observability are highly sought after.

How Can GTP Compete

• Integrate Continuous Performance Testing

  • Provide built-in pipelines or plugins that make it easy to run small, targeted performance tests in each stage of the SDLC.

• AI-Augmented Features

  • Offer anomaly detection, root cause suggestions, and user behavior modeling.

  • Streamline test planning (auto-select critical user flows, load patterns).

• Robust APM/Observability Integration

  • Create seamless dashboards and correlation tools that tie test results to system/infrastructure metrics.

• Ease of Use & Reporting

  • Simplify scripting, reduce overhead for testers, and deliver business-friendly insights.

Webinar 5: AI Basics for Testers

About Company/Product

• Company: BlinqIO

• Product: An AI-driven test automation platform

Objective of the Webinar

• The webinar aimed to demonstrate how generative AI simplifies the transition from manual testing to automated test engineering, removing the need for deep coding skills. Key objectives included:

• Explaining how AI changes the test automation landscape.

• Showcasing a live demo of Blink IO’s AI-driven tool to generate and maintain test scripts.

• Highlighting benefits such as reduced maintenance, faster script creation, and continuous integration (CI) compatibility.

Presenting the Webinar

• Tal Provided high-level insights on how AI fundamentally alters test automation practices.

• Sapneesh (Head of QA) Led the live demo and explained the technical steps for creating AI-generated tests.

• Joe (Test Guild Host) Facilitated the discussion, asked clarifying questions, and moderated the Q&A.

Brief Summary of the Webinar

• AI Fundamentals for Testers

  • Generative AI (“Gen AI”) can act as a virtual “test engineer,” creating, updating, and maintaining automation code with minimal user input.

  • Traditional coding requirements (e.g., knowledge of JavaScript, Selenium, page object models) can be largely bypassed with AI.

• Manual to Automation Transformation

  • AI drastically reduces the technical barrier: testers with manual background can record user flows and get production-ready code instantly.

  • AI can self-heal scripts if the UI changes, using business logic as a guide.

• Blink IO Tool Demonstration

  • Created a new project, added the application URL (e.g., Salesforce).

  • Recorded a user flow (login, form fill, assertions) while AI generated code (Playwright + CucumberJS) behind the scenes.

  • Showed how to incorporate secure credentials, dynamic test data (via Faker, CSV, API calls), and how to run tests locally or via CI/CD.

  • Demonstrated debugging in VS Code if advanced customizations are needed.

• Q&A and Key Takeaways

  • AI-based approach is not “record-and-playback”: code is robust, open source, and can be integrated into any pipeline.

  • AI can automatically maintain scripts and update locators or logic if the application changes.

Features and Technical Aspects

• AI-Driven Script Generation

  • Users simply click through a scenario; AI creates Playwright + CucumberJS code with well-structured locators and functions.

• Self-Healing & Maintenance

  • If locators or UI change, AI can “recover” and update code without manual intervention.

• Test Data Management

  • Integrations with Faker for random data, CSV files, or APIs for dynamic data.

  • Secure credential storage for passwords or sensitive info.

• CI/CD Integration

  • Simple Linux-based commands can be placed in Jenkins, GitHub Actions, Azure DevOps, etc.

  • Full debugging support in VS Code or any local environment.

• Open Source Code

  • Generated code is non-vendor-locked. It can be customized, extended, or version-controlled.

Main Advantages and Differentiators

• Reduced Coding Barrier

  • Manual testers can produce stable, production-grade scripts without deep programming knowledge.

• Faster Test Creation

  • Record once, instantly generate code. AI-based approach eliminates repetitive scripting tasks.

• Stability & Self-Healing

  • AI uses user-facing locators and business logic to auto-fix broken scripts after UI changes.

• Scalability & Collaboration

  • Integrates with test management systems (JIRA, TestRail, etc.), supports multi-user workflows, and offers advanced debugging in VS Code.