Traditional performance testing focuses heavily on metrics like response time, throughput, and error rates. However, in today’s complex digital landscape, effective performance testing requires a broader perspective that considers user experience, business impact, and system resilience.

This talk explores how to move beyond the numbers to evaluate real-world scenarios, enhance observability, ensure inclusivity, and build fault-tolerant, sustainable systems. By adopting this holistic approach, organizations can create reliable, user-centered applications that align with business objectives and thrive in dynamic environments.

Talk will be based on how we can make use of LLMs to enhance classical software mutation testing to find code vulnerabilities

• Enhanced mutation testing to target code bugs
• Optimizations to make mutation testing work with LLMs

How product usage analytics can be used to create knowledge graph and solve customer churning issue?

• Insights of product usage is very crucial
• Take these feedbacks into account while drafting testing strategy

AI is transforming the software quality engineering industry by automating routine tasks and enabling smarter testing approaches. Its integration into software development has profoundly reshaped the field of Quality Engineering (QE). This presentation explores the evolving role of QE in the AI era, addressing the challenges, opportunities, and strategies for achieving excellence in AI-driven environments. By examining the intersection of AI and QE, it provides valuable insights into best practices for maintaining high-quality software and equips QE professionals with the knowledge to excel in an AI-dominated landscape.

Following are the Key Take Aways from the talk:

• Understanding of Evolution of QE with AI Integration
• Challenges in Adopting AI in QE
• Opportunities Presented by AI in QE
• Future Trends in AI and QE
• Continuous Learning and Adaptation

This tech talk will explore the critical role of automated testing in modern DevOps and Agile environments. As software development cycles become increasingly rapid, the integration of automated testing is essential to maintain high-quality standards while accelerating delivery timelines. The session will cover the principles of test automation, the benefits of integrating automated testing into CI/CD pipelines, and the challenges and best practices for achieving seamless integration. Attendees will gain insights into the latest tools and frameworks that facilitate automated testing and how to align testing strategies with Agile methodologies to ensure continuous improvement and faster feedback loops.

• Understanding the Role of Automated Testing: Learn why automated testing is a cornerstone of DevOps and Agile practices and how it enhances software quality and reliability.
• Integration with CI/CD Pipelines: Discover how to effectively integrate automated testing into Continuous Integration and Continuous Deployment (CI/CD) pipelines, ensuring faster and more reliable releases.
• Best Practices for Implementation: Gain insights into the best practices for implementing automated testing within Agile teams, including strategies for test automation coverage, test maintenance, and balancing manual and automated tests.
• Tools and Frameworks: Explore the latest tools and frameworks that support automated testing in DevOps, and understand how to choose the right ones based on project needs and team capabilities.
• Overcoming Common Challenges: Identify common challenges faced during the integration of automated testing and learn practical solutions to overcome them, ensuring a smooth transition and continuous improvement.
• Enhancing Collaboration Between Teams: Understand how automated testing fosters better collaboration between development, operations, and testing teams, leading to more efficient workflows and faster delivery of high-quality software.
• This talk is ideal for software developers, QA engineers, DevOps practitioners, and Agile team members who are looking to enhance their testing strategies and streamline their development processes through automation.

Resilience-driven testing: Discover how chaos engineering and fault injection can proactively build resilience in microservices by identifying weaknesses before they reach production.

Scalable testing frameworks: Learn strategies for designing a scalable testing framework tailored for distributed microservices, integrating automated tests, contract testing, and CI/CD pipelines.

Observability and collaboration: Gain insights into using observability tools for real-time diagnostics and fostering a collaborative culture where resilience is a shared responsibility across teams.

In this presentation, we explore the evolving landscape of software testing with the advent of generative AI tools like ChatGPT and Copilot. While these technologies promise to automate many aspects of software testing, they also raise existential questions about the future of human testers. Drawing insights from Gerald Weinberg’s “Perfect Software and Other Illusions About Testing,” we will debunk the myth of complete automation and highlight the irreplaceable value that skilled testers bring to the software development process. This discussion will focus on the unique capabilities of human testers to understand complex scenarios, manage risk, and transform data into actionable intelligence, emphasizing that testing is not just a technical task but a deeply human and political process.

• Debunking the Automation Myth: Complete automation of software testing is not feasible due to the vast complexity of possible test scenarios and the need for human judgment in defining essential test cases.
• The Unique Role of Human Testers: Humans bring critical thinking, creativity, and ethical considerations to testing, which are essential for understanding complex interactions and making informed decisions about risk and quality.
• Testing as Intelligence Gathering: Testing serves as a crucial information-gathering process that informs development decisions, risk management, and strategic planning, highlighting the tester’s role as a key contributor to project success.
• The Political Nature of Testing: Testing involves navigating human relationships and emotions, making it inherently political. Testers must balance technical objectives with interpersonal dynamics to ensure successful project outcomes.
• Embracing Technology with Human Insight: While generative AI can aid in repetitive tasks, combining technology and human insight is necessary to achieve efficient, effective, and ethical software testing.

In a world where AI co-pilots and coding tools are redefining software development, a new paradigm is emerging: AI systems working in tandem to both generate and validate code. The proposed speaker TestSprite CEO Yunhao Jiao, former Senior Software Engineer at AWS, brings valuable insights from his AWS engineering experience to demonstrate how AI testing tools complement AI coding assistants and create a complete quality assurance solution. This session will delve into the dual role AI plays in accelerating development while introducing potential risks, such as hidden bugs, “black box” code and logic inconsistencies. By leveraging both AI-powered development and testing tools, teams can achieve both speed and reliability in their software development process.

Key topics include:

The Strengths of AI in Coding: How AI accelerates repetitive tasks, assists workflows, and is a learning tool for junior developers.

Where AI Code Falls Short: Common challenges with debugging and co-pilots lack general logical reasoning, relying on pattern recognition rather than true understanding.

Balancing Speed and Quality: Why autonomous testing tools like TestSprite are essential for rigorous quality control, with capabilities like automated test generation, debugging support, and edge-case handling. Autonomous AT testing tools also provide a safety net by catching complex issues that AI coding assistants might miss.

Attendees will learn how to:

• Identify when and how to supplement AI coding tools with autonomous testing to catch subtle errors early.
• Develop strategies to mitigate the limitations of AI-generated code and prevent logic-based bugs.
• Implement TestSprite’s autonomous testing solutions to enhance speed without sacrificing reliability.
• Future-proof their workflows by integrating autonomous testing into their development lifecycle for higher quality code in less time.

The proposed speaker will share practical methods to bridge the gap between AI-powered coding and dependable code quality, equipping attendees with insights and considerations into the future of AI-driven development and testing. This topic also aligns well with the ongoing evolution of AI in testing and will appeal to attendees looking for insight into cutting-edge AI testing tools.