Artificial Intelligence is already transforming the software testing landscape—accelerating processes and augmenting human capabilities. In this session, we will explore where AI is delivering real value today, where its current limitations lie, and what that means for testers and teams. We will also look ahead at how AI is likely to shape the future of software testing—raising both exciting opportunities and critical questions for the industry.

Redefining what Software Testing is and how it is being used to ensure a customer centric approach test result is delivered to your customers in areas that are unknown

• Delegates will be able to understand the needs and essentials of Software Testing in their industry
• Software Testing not only just plays the role of testing a software but to provide a better all round quality assurance
• Outcome of software testing will not only test defects of a system, but to ensure a more customer centric approach when it comes to having the results presented

This session will discuss automation, test data management, test scripting, exploratory testing enhance testing with Large Language Model (LLM) Identify opportunities to improve test quality with AI Construct test automation with AI tools Ideas during exploratory testing using AI tools Leverage AI tools to aid the design process of new features Improve testability with AI tools Maximize output with prompt engineering

Leverage AI and LLMs and GenAI to improve your software and data testing efficiency Learn new tools and tricks for Testing

Quality isn’t just about finding bugs anymore—it’s a key factor in business success and development speed. Many teams face challenges like disconnected efforts, slow feedback, and lack of a clear strategy, leading to delays, higher costs, and inefficiencies.

This session will help you build a quality roadmap that brings business, operations, and development teams together. We’ll cover a simple 4-step framework—Strategic Planning, Prioritization, Execution, and Continuous Adaptation—to create a scalable and efficient quality process.

We’ll also explore how AI, automation, and real-time monitoring can strengthen quality assurance, making teams more efficient and resilient in today’s fast-moving digital world.

• The critical role of a quality roadmap in business success and development efficiency
• A structured 4-step framework to build and implement a quality roadmap
• Best practices for aligning quality goals with business and operational priorities
• Strategies to reduce costs and optimize workflows through automation and AI-based quality engineering
• Practical insights on measuring quality success using business, operational, and technical KPIs

AI bias can negatively influence decision-making in hiring, healthcare, finance, and more, leading to unfair outcomes, regulatory challenges, and reputational damage. This session explores how to detect bias in AI models with practical techniques. Whether you’re building AI models, testing them or auditing them for quality assurance, will raise your awareness on this topic and provide you with actionable strategies for detecting and mitigating bias in AI systems.

• Real-world examples of AI bias
• Importance of identifying AI bias
• Several practical techniques to detect AI bias

Quality isn’t just about finding bugs anymore—it’s a key factor in business success and development speed. Many teams face challenges like disconnected efforts, slow feedback, and lack of a clear strategy, leading to delays, higher costs, and inefficiencies.

This session will help you build a quality roadmap that brings business, operations, and development teams together. We’ll cover a simple 4-step framework—Strategic Planning, Prioritization, Execution, and Continuous Adaptation—to create a scalable and efficient quality process.

We’ll also explore how AI, automation, and real-time monitoring can strengthen quality assurance, making teams more efficient and resilient in today’s fast-moving digital world.

• The critical role of a quality roadmap in business success and development efficiency
• A structured 4-step framework to build and implement a quality roadmap
• Best practices for aligning quality goals with business and operational priorities
• Strategies to reduce costs and optimize workflows through automation and AI-based quality engineering
• Practical insights on measuring quality success using business, operational, and technical KPIs

Passionate Technologist specialization in High Performance computing
Interest: Parallel & concurrent programming, Java, Algorithmic electronic trading &
Real-time Trading System and FIX tech.
Contributing/Research in Financial Engineering and Financial Mathematical models (Stochastic/Brownian Equation/PDE).

Luis Carlos Cruz Huertas is an Executive Solutions Enterprise Architect with 20+ years of experience leading global technical and business teams. He specializes in AI, Big Data, Cloud Solutions, and systems integration, optimizing processes for Banking, Finance, and Telecoms across various industries.

Brynley Scully is CTO at Tescom, Singapore, with deep expertise in tech leadership, software testing, QA, and project management. He holds multiple certifications and is passionate about building testing excellence and mentoring professionals.

Bringing Agility (DevOps) | Quality (TestOps) | Visibility ( Metrics) | to Software Engineering

Over two decades of enterprise IT experience across all of software life cycle with special focus on DevOps & Agile transformations, Project & Quality management, Software tool chain integrations, infrastructure management & supporting mission critical operations in production

Love to dig deep into technology and create engaging presentations for the developer community. Contributor to open source projects and advocate for open source ideology. Fast learner, continuously seeking to understand challenging technologies in order to improve overall skills and abilities.