Speaker: Philip Lew, XBOSoft., USA

Synopsis:

Has your testing career plateaued despite mastering the latest technical skills? In our rush to adopt AI, learn Python, or earn new certifications, we’ve overlooked the fundamental human skills that differentiate successful leaders from individual contributors in the software testing profession.

While technical expertise is necessary, it’s rarely sufficient for career advancement. The critical difference between staying an individual contributor and becoming an influential leader often comes down to one undervalued skill set: Emotional Intelligence (EI).

As testing professionals return to collaborative office environments post-pandemic, many are discovering a skill gap. Years of remote work have eroded our ability to navigate in-person team dynamics, and build relationships through spontaneous interactions—precisely the skills that differentiate human testers from automated systems.

In this keynote, Phil shares his transformation from a technically focused engineer to a successful entrepreneur, corporate executive, and testing leader. He reveals the specific EI skills that propelled his career forward when technical knowledge alone couldn’t and demonstrates how these capabilities can be systematically developed using an engineering mindset.

You’ll discover:

• • How EI can be your career “multiplier,” amplifying the impact of your technical testing skills
  • Why face-to-face collaboration skills have become the new competitive advantage in the post-pandemic workplace
  • A structured framework for developing EI that appeals to the analytical mindset of testing professionals
  • Practical techniques for remaining emotionally centered during testing challenges, including:

o Managing imposter syndrome during complex test implementations

o Responding constructively to failed test cycles or critical bugs

o Navigating rejection of test approaches or automation proposals

o Transforming passive-aggressive team dynamics into productive collaboration

o Building stakeholder support for testing initiatives

o Addressing resistance from developers or product owners

o Creating psychological safety within cross-functional testing teams

Unlike technical skills with clear learning paths, EI is rarely taught systematically in testing careers—yet it’s often the determining factor in who advances to leadership roles. Through real-world examples and practical frameworks, you’ll learn how to recognize emotional patterns in workplace testing scenarios and apply specific EI techniques to transform challenges into opportunities for growth.

For testers who aspire to lead, this session offers a blueprint for developing the human-centered skills that AI can never replicate—and that will remain essential even as testing tools become increasingly automated.

Speaker: Vipul Kocher, TestAIng, India

Synopsis:

AI systems, including large language models, often rely on neural networks that are highly complex and difficult for humans to fully comprehend, raising critical questions about their security, integrity, and reliability. How can we place trust in such systems?

In this keynote, we’ll examine these challenges and explore solutions through techniques like transparency, explainability, and debiasing, alongside proactive measures such as security and privacy stress-testing. Learn how these approaches can ensure AI-driven software delivers both exceptional performance and unwavering trustworthiness.

Speaker: Simon Frankish, Experimentus, UK

Synopsis:

Take a closer look at how AI technologies can be leverage on to enhance/complement SQA/testing processes in organisations, using TMMi framework as a reference model.

Speaker: Zarina Zain, Cognizant, Malaysia

Synopsis:

TBC

Speaker: Robert Rose, University of Memphis, USA

Synopsis:

Artificial Intelligence (AI) systems have significantly reshaped numerous sectors through innovation and improved operational efficiency. However, their increasing adoption introduces profound ethical challenges, such as fairness, accountability, transparency, and bias. To responsibly leverage AI’s transformative potential, organizations must adopt rigorous methods to systematically assess ethical adherence.

This workshop offers participants a practical exploration of a comprehensive test case generation framework designed explicitly for evaluating AI systems against ethical standards. Participants will engage in interactive sessions that delve into core ethical principles, including fairness, accountability, transparency, privacy, and non-discrimination. Utilizing the Goal-Question-Metric (GQM) methodology, attendees will learn to articulate clear ethical goals, formulate targeted assessment questions, and apply suitable quantitative, qualitative, and hybrid metrics for comprehensive ethical evaluation.

Through practical exercises, collaborative discussions, and expert insights, attendees will gain hands-on experience developing high-level test cases tailored for formative and summative evaluations in both artificial and real-world scenarios. Additionally, participants will learn how to effectively conduct qualitative interviews with stakeholders such as AI ethicists, developers, regulators, and end-users, enabling richer insights into ethical considerations and evaluation practices.

This workshop is ideal for AI developers, ethicists, policymakers, and industry leaders committed to ensuring ethical integrity in AI deployment, enhancing transparency, and building public trust in AI technologies.

Speaker: Philip Lew, XBOSoft., USA

Synopsis:

Effective communication is the skill multiplier that transforms technical expertise into professional leadership. This comprehensive 4-hour workshop focuses on the two most critical active communication skills for engineering professionals: strategic listening and compelling speaking.

Many engineers excel at technical problem-solving but struggle with the interpersonal dynamics that make their expertise truly impactful. This workshop addresses both sides of verbal communication – how to extract maximum value from conversations through advanced listening techniques, and how to present ideas with clarity and persuasion through systematic speaking skills.

The core philosophy integrates proven methodologies: “Practice With Feedback Makes Perfect” for skill development, combined with systematic approaches to both gathering information through listening and delivering information through speaking.

Intended Audience

This workshop is designed for engineers, test professionals, and technical leaders who want to:

• Improve effectiveness in meetings, client interactions, and stakeholder communications
• Extract better information from requirements gathering and problem-solving discussions
• Present technical concepts more clearly and persuasively
• Build stronger professional relationships through better conversation skills
• Overcome public speaking anxiety through structured approaches
• Develop systematic methods for both gathering and sharing information

Speaker: Philip Lew, XBOSoft., USA

Synopsis:

Overview

Software quality metrics are essential for understanding the health and effectiveness of our development and testing processes. However, many organizations struggle to implement metrics that drive business value rather than merely collecting data for its own sake. This workshop addresses this critical gap by teaching participants how to create, implement, and leverage meaningful metrics that connect directly to business objectives.

As artificial intelligence transforms software testing practices, this workshop also explores the unique challenges and opportunities of measuring AI effectiveness in quality assurance workflows. Participants will learn foundational metric principles and approaches for evaluating AI-powered testing initiatives.

Why Metrics Matter

When implementing software quality metrics, we must first understand their purpose and audience. Are these metrics intended to measure people, processes, product quality, or progress toward specific objectives? QA managers typically want to deliver productivity metrics to management, while executives may focus on customer satisfaction indicators or cost-related measures.

This disconnect often leads to metrics that fail to demonstrate value to decision-makers. By developing quality metrics with clear, actionable objectives tied to business goals, you can bridge this communication gap and ensure your metrics drive meaningful improvements rather than becoming forgotten spreadsheets.

Workshop Benefits

This half-day course teaches participants how to develop and implement a metrics framework for development and QA organizations. You’ll learn to:

• Align quality metrics with strategic business objectives
• Create metrics that drive action rather than merely reporting information
• Communicate metrics effectively to different stakeholders
• Avoid common pitfalls that lead to unintended negative behaviors
• Adapt traditional metrics for AI-enhanced testing environments

Measuring AI Effectiveness in Software Testing

As organizations incorporate artificial intelligence into testing workflows, new measurement approaches become necessary. This workshop covers essential metrics for evaluating AI testing initiatives and considerations that impact AI testing effectiveness.

Who Should Attend

This workshop is valuable for quality assurance managers, test leads, development managers, product owners, and other stakeholders involved in software quality decisions. Both testing professionals looking to implement AI solutions and those seeking to improve their traditional metrics approaches will benefit from this comprehensive curriculum.

Join us to transform your quality metrics from ignored spreadsheets into powerful tools that drive meaningful business improvements and prepare your organization for the evolving world of AI-enhanced quality assurance.

Speaker: Vipul Kocher, TestAIng, India

Synopsis:

In this workshop we will understand the reasons for not trusting the AI and the type of solutions where trust in AI is absolutely. essential. We will look at

• • Defining “trust” in AI and what it means for decision-making.
  • Putting human-in-the-loop and potential pitfalls.
  • Examples of AI systems where trust can be easily compromised and their consequences.
  • Frameworks for assessing the reliability of AI models in critical applications (e.g., autonomous vehicles, healthcare).

Speaker: Vipul Kocher, TestAIng, India

Synopsis:

Common security threats in AI systems, including adversarial attacks. How adversarial attacks manipulate AI models and undermine trust. Defensive mechanisms to protect neural networks, such as adversarial training and robust optimization. The role of secure hardware (e.g., trusted execution environments). As AI systems become integral to decision-making across industries, ensuring their security critical to maintaining trust and reliability.

In this session we explore myriad security threats targeting AI, with a focus on adversarial and backdoor attacks, model inversion and stealing attacks and data poisoning on one side and deep fakes on the other side. We will also look at techniques to counter these risks including adversarial training and data testing.

Speaker: Simon Frankish, Experimentus, UK

Synopsis:

TBC

Speaker: Simon Frankish, Experimentus, UK

Synopsis:

TBC

Speaker: Herman Md Tahir, Appfuxion Consulting, Malaysia

Synopsis:

Designing and deriving test cases had been quite a challenge for testers, especially in managing expectations (for e.g. test coverage requirements, high quality test cases) that are set by the product owner. TMMi through the process area of Test Design and Execution is giving emphasis on the activities to derive good quality test cases through application of suitable test design techniques. Common test design techniques for e.g. Equivalent Partitioning (EP), Boundary Value Analysis (BVA) and many more can be time-consuming in analyzing (the requirements), designing and deriving test conditions and test cases, when performed manually. AGIS (Advanced Generative Intelligence System) assists this challenge by utilizing the advancement of AI in generating test cases. AGIS does this by optimizing the test cases generation while ensuring test coverage at the same time. AGIS was tested in auto-generating test cases with comprehensive 100% test coverage of the requirements outline in the FSD (e.g. Functional Specification Document), comprehensive testing coverage of both positive and negative test cases with detailed test steps and expected results and demonstrated AI ability to understand screen images and process flow in the FSD. This could save testers around half of their time in deriving test cases from requirement documents including review and prioritization. AGIS also provides chatbot to support AI chat-based query and task initiation (e.g. test case generation and test status reporting). Key workshop takeaways 1. Re-visit Test Analysis and Design (using test design techniques) from TMMi Process Area 2.4 (Test Design and Execution) a. Participants will be introduced to (or review/recall) test analysis and design flow b. Participants will be introduced to (or review/recall) test design techniques (in particularly the Equivalent Partitioning (EP) and Boundary Value Analysis (BVA)) 2. Application of AI technology in software testing a. Participants will be introduced to trends of AI applications in software testing b. Participants will be introduced to AGIS and how AGIS can help with software testing 3. Demo on AGIS a. Participants will be able to experience AGIS capabilities through a demonstration session.

Speaker: Pricilla Bilavendran, Synapse, Malaysia

Synopsis:

AI is everywhere. We cannot pretend to ignore it. In recent years, LLMs have improved their responses. As testers, we should learn about and embrace artificial intelligence to explore how it can assist us in improving test efficiency. Rather than treating it as an enemy, we might make friends and harness the power of AI. Always remember, Garbage in, garbage out. So if you are good at asking you needs rightly, GenAI can produce quality outputs. Thus, prompting becomes one of the key skills in this era. 

In this tutorial, we will explore the basics and core concepts of prompting, advanced prompting techniques, how prompts could help Software Testers and testing, designing better prompts, and comparing the prompt responses using multiple tools, and hands-on exercises to create prompts. No prior AI expertise is needed. Whether you’re a functional tester, SDET, or test lead, this session will boost your productivity and creativity. Walk away ready to turn GenAI into your testing co-pilot, prompt by prompt.