Systems | Development | Analytics | API | Testing

In this session, Ashwini Lalit delves into how Quality Engineering teams can leverage a data-driven approach to Value Stream Mapping (VSM) to achieve quality excellence. Traditional workflows often mask inefficiencies and bottlenecks behind agile ceremonies and feel-good optics, making it challenging to gauge the true readiness of release deliverables. By combining VSM with agile principles and DevOps tools, Ashwini demonstrates how to visualize workflows holistically, streamline production releases, and transition from reactive to proactive quality processes.

Escaped bugs can break more than your code—they can break the bank, too. In this episode of Test Case Scenario, Jason Baum, Evelyn Coleman, and Marcus Merrell break down the connection between software quality and business impact. They talk about how to approach postmortems, why some features matter more than others, and how technical teams can align their work with business goals. You’ll also hear real-life stories of bugs that cost companies millions and learn practical tips for balancing technical needs with business priorities in industries like retail, finance, healthcare, and government.

#Automation #SoftwareTesting #QA

In this session, Gil Zilberfeld highlights a critical challenge in test automation—the creation of "Frankenstein tests." These tests combine programming languages, frameworks, and domain-specific languages into a mishmash that is difficult to read, interpret, and maintain. While this approach may seem functional initially, it often leads to complex, fragile tests that become a burden as projects evolve. Gil emphasizes the importance of addressing this issue to prevent the "monster" of unmanageable tests from overwhelming your testing strategy.

In an era increasingly driven by artificial intelligence, ensuring ethical development and testing practices is more crucial than ever. In this thought-provoking session, Gaurav Khurana will explore the core principles of Responsible AI, including transparency, accountability, fairness, and privacy. These principles act as a foundation for building trust, mitigating algorithmic biases, and ensuring responsible data usage, ultimately fostering an AI ecosystem that prioritizes human welfare and respects privacy rights.