Join Sunita McCoy as she takes you courtside to explore how quality engineering can balance speed, stability, and style in today’s high-stakes software game.
Discover how to build a quality-first culture, empower teams as playmakers, and streamline workflows with automation, CI/CD, and lean testing practices.
Learn how to strengthen systems with performance engineering, boost resilience with observability, and gain traction with platform engineering and DevX to scale quality across the enterprise.
Don’t miss out, book your free spot now
How do you balance the need for testing speed with the importance of maintaining stability and reliability?
What practices or tools help teams add ‘style’—meaning innovation and creativity—without compromising quality?
How can you persuade stakeholders to dedicate time to sustained conditioning instead of only chasing immediate wins?
How do you get buy-in from leadership and the team for long-term preparation when the environment rewards speed and quick scoring?
What are the most effective ways to integrate AI and automation to increase velocity in QA and DevOps?
How do you convince leadership and the team to invest time in that long-term conditioning when the pressure to just score, and score fast, is relentless?
Which matters more for your org today, speed of delivery, system stability, or product style?
What does digital transformation look like (and how does it function) in the post-AI era vs. before it?
How can organizations integrate user feedback and analytics data into the QE and QA processes to drive continuous improvement in style and user experience?
If quality were a basketball game, are your teams playing defense (catching bugs) or offense (preventing them)?
How do you build confidence in production releases when pressure for speed is high?
How should performance engineering be integrated early in the delivery pipeline to ensure endurance?
What practices ensure stability and reliability when pushing frequent releases in continuous integration pipelines?
If automation can already predict shoe wear patterns, are durability complaints becoming excuses for poor maintenance?
What are best KPIs for transformation, if we also consider a new greenfield project?
How can QE teams effectively use AI and automated testing to accelerate the QA process without compromising on depth and quality coverage, making sure that “Speed” doesn’t sacrifice “Stability”?
How can orgs best implement a robust QE strategy for proactively preventing and addressing potential vulnerabilities and security risks (Stability) throughout the development lifecycle?
How would you evaluate quality in terms of speed, stability and style?
What strategies ensure that “style” in quality—like user experience and design validation—gets the same attention as functional stability?
