Use Microsoft 365 to build automation framework. How Power Automate for Desktop and Excel can help you automate testing with no additional tool investment.

45 year software development consultant with proven practical test automation and quality assurance skills. Application and automation experience for publishing, financial, entertainment, and banking firms in the New York metropolitan area as an employee and as a consulting professional. Firms include SiriusXM, Dunn & Bradstreet, Readers Digest, Citibank, and E. F. Hutton.

A quality assurance roadmap can help you deliver quality software to your customers in a consistent, predictable and repeatable manner. A quality assurance roadmap is a time-based plan that defines where you are, where you want to go, and how to get there. It is a highly detail-oriented discipline that charts the plans, processes, and technologies implemented by an organization to ensure product quality. Usually, it helps QA teams in determining the long-term vision for the company by taking a proactive approach. We will discuss in this session the process of creating one, and look at some real world examples.

• What is a Quality Assurance Roadmap?
• What are the key steps in a QA process?
• Define your Objectives
• Do an initial quality assessment
• Engage Stakeholders
• Be agile – flexibility and adaptability are key
• Empathize Continuous Improvement
• Real World Examples

We have many frameworks for testing, automation frameworks like Pytest, Robot, or Junit, tracking systems like Xray, Testrail, or Qase. We have dashboarding, automatic notifications, code coverage tools and a wealth of other resources. But what happens if teams don’t adopt them? What if code coverage is being circumvented? How do you bend the technology at hand to the purpose of convincing teams to raise their quality bar?

• Quality is a human first endeavor
• Contract testing is an important gating mechanism for regression, but also an invitation for conversation and a way to help break silos.
• Hard quality metrics can often backfire
• Quality Engineering is a surprising amount of talking to people

Inevitably in any project there are delays or roadblocks you have to overcome, and when it comes to obtaining timely access to usable and sound data it’s no different. Perhaps you’ve asked yourself: Do I just create my own data to test this new feature? Or do I wait? And if I wait, how useful will the data be that I receive anyway?

In this session we’ll explore how to create team-wide rapid data accessibility by combining database virtualization, data anonymization and even a little AI. We’ll also examine how to find the right balance between generating mock data, anonymizing production data and what the industry trends reveal about the impact.

Walk away knowing how to:

• Improve data accessibility for everyone involved
• Anonymize data using masking and AI
• Navigate these hurdles using effective strategies

Medical device software test comes with a hurdle of compliance and regulatory boundaries that are complex and challenging. Thankfully, Test-Driven-Development variants help navigate this challenge without disrupting the traditional medical device product development life cycle.

Challenges in Medical device Agile software development/test practices

• Compliance and Standards expectations
• Test-Driven Development, AAMI TIR45 Guidance
• Examples from the medical device test automation

Vinod Kashid will walkthrough his experience building end-to-end automation for performance testing and engineering. His team was tasked to do daily performance testing activities for a critical application and business executives were expecting detailed application performance outcomes daily. Manual performance test execution and analysis were time consuming processes and were delaying the overall run time for final report. Business stakeholders wanted to implement zero touch automation from performance test execution to analysis and reporting (including deep-dive analysis into backend layers).

This session will help team understand how to integrate Performance test executions in any DevOps pipeline. This session will also explain how to automate test reporting using tools like Grafana

Imagine a future where Quality Assurance (QA) isn’t just about testing software—it’s about collaborating with intelligent AI agents to shape seamless, adaptive digital ecosystems. Inspired by The Matrix, this session envisions a world where testers become architects of quality, wielding tools as intuitive and persistent as Agent Smith to uncover hidden vulnerabilities and predict system behaviors.

Through the lens of healthcare privacy, robotics, e-commerce, and IoT, we’ll explore how hyperautomation and predictive analytics are evolving into a unified QA network that scales effortlessly, anticipates flaws, and transforms data into actionable insights. In this future, AI agents act as tireless allies, enhancing observability, automating complexities, and freeing testers to innovate at the edges of human creativity.

Join us as we journey into the QA Matrix—a realm where human intuition merges with machine precision, testing becomes anticipatory, and quality is no longer an endpoint but an adaptive, living standard. This is your glimpse into the future of QA. Plug in and be part of what’s next.

I would like to provide an overview of the possibilities for API testing using Python and Pytest. During our time at NetApp, my colleague and I (Aleksey Treskov – presenting together – current engineer at Workday) collaborated to design, architect, and implement a large-scale Python testing framework for NetApp’s Kubernetes based storage solutions. Together we would like to share our experiences, including the challenges we faced and the successes we achieved, so that other SDETs and QA professionals can benefit from our journey.

We want the audience to understand the simplicity of getting started with Pytest and Requests while learning best practices for organizing test cases and verifying CRUD API operations. Additionally, we would like to showcase techniques for building common libraries and helper methods, as well as exploring strategies for test framework parameterization and test suite entry points.

I would like to provide an overview of the possibilities for API testing using Python and Pytest. During our time at NetApp, my colleague and I (Aleksey Treskov – presenting together – current engineer at Workday) collaborated to design, architect, and implement a large-scale Python testing framework for NetApp’s Kubernetes based storage solutions. Together we would like to share our experiences, including the challenges we faced and the successes we achieved, so that other SDETs and QA professionals can benefit from our journey.

We want the audience to understand the simplicity of getting started with Pytest and Requests while learning best practices for organizing test cases and verifying CRUD API operations. Additionally, we would like to showcase techniques for building common libraries and helper methods, as well as exploring strategies for test framework parameterization and test suite entry points.