All engineering teams should be nimble, and your testing team is no exception. In this current climate, it’s even more important to ensure that test engineers are as optimized as possible in order to quickly pivot to best support and enable the delivery efforts of the development. Although sometimes overlooked, the testing team can be a key component to not only inform the teams on quality of the technical solutions, the right people can also be influential in the overall quality of the SDLC. In this session, we will talk about three different approaches when building and maintaining a software quality team – centralized, de-centralized and hybrid and how each has its own nimble characteristics Whichever approach you are taking on your testing team, each has its pros and cons and we will have a thoughtful discussion on them as they pertain to hiring, assessing overall testing and quality, and how you can ensure the team is operating in the most efficient way possible.

• Agile Testing
• Test Leadership
• Test Transformation

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

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.

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

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

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