Artificial Intelligent (AI) is reshaping the future of Quality Assurance (QA), advancing beyond traditional automation to deliver smarter, faster, and more reliable testing. This talk examines how AI transforms QA practices, harnessing technologies like machine learning, natural language processing (NLP), and predictive analytics to address complex systems and speed up delivery. It provides practical insights and real-world applications, highlighting AGEST’s AI-driven testing approaches within a vision for next-generation testing. Learn how AI can enhance QA strategies and equip teams for future challenges.

• AI’s Transformative Power: Understand how AI accelerates testing, enhances coverage, and accurately predicts defects.
• Practical Applications: Discover real examples of AI-driven testing approaches from AGEST and industry leaders.
• Overcoming Challenges: Gain strategies for addressing data quality issues, bridging skill gaps, and ensuring ethical AI use.
• Future-Ready QA: Learn how to build an AI-powered team and seamlessly integrate AI into DevOps pipelines.
• Vision Forward: Embrace the role of QA as a strategic driver in delivering flawless software and fostering innovation.

The topic can be presented with below aspects:

• Overview of Agile and Test Automation
• Challenges in Agile Testing
• Test Automation Strategies for Agile
• Selecting the Right Tools for Agile Test Automation
• Best Practices for Test Automation in Agile
• Case Studies/Real-life Examples

• Adaptability: Agile is about continuous improvement. As your project evolves, so should your test automation approach. Stay flexible and responsive to changes.
• Collaboration is Key: Agile success depends on collaboration between developers, testers, and business stakeholders. Foster a culture of communication to ensure quality is embedded in every part of the process.
• Test Automation is a Journey: Don’t aim for full automation all at once. Start small, automate critical areas, and scale as needed. Consistently maintain and improve the test suite to keep it relevant and effective.
• Quality and Speed: Test Automation allows you to deliver high-quality software faster. When integrated into Agile, it supports quicker feedback cycles, better test coverage, and faster release cycles.

In this topic, I’d like to share how AI is leveraged in automation testing and check whether it can reduce the effort for automation test.

• Challenges in automation testing
• How AI supports to solve the challenges
• Top AI tools for automation test

Aware of the ability of AI in automation test

• Popular AI tools and idea them in the real project

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

Test automation is evolving with AI-driven frameworks, cloud-based testing for scalability, and scripting advancements in Python, JavaScript, and TypeScript. These trends enhance speed, reliability, and CI/CD integration, shaping the future of software testing.

• Trends in QA Automation – Emerging practices, AI-driven testing, and the shift toward autonomous testing.
• Advanced Technologies Enhancing Testing – AI, and cloud testing solutions.
• Adapting to Trends & Modern Technologies – Upskilling, embracing AI-assisted testing, and integrating automation into CI/CD.
• Challenges in Test Automation & Solutions
• Test Automation Tools: Benefits & Limitations

Everyone is jumping on the bandwagon to use Artificial Intelligence (AI) and Machine Learning (ML) to help automate testing processes in white box or black box testing. In Parasoft, we use AI & ML to enhance the testing processes to optimize your automated tests not to replace the Tester or QA. In fact, we would like to make their jobs easier and faster through the use of AI & ML.

As Forrester has indicated, “Parasoft doubles down on infusing AI capabilities into its platform. It has undisputed strengths in API testing made easy with AI and integrated with its service virtualization offering. Shift-Left performance testing for converged functional and performance testing and its long-time mature analytical reporting are also strong features….”
“Parasoft can rave about its ‘built here, not acquired’ product and innovation approach, which strengthens a consistent experience across all testing types.” (parasoft.com/analyst-research/the-forrester-wave-continuous-automation-testing-platforms-summary)

How Parasoft’s use of AI & ML is able to improve the Coding Practices, Code Coverage and Enhancement of Test Cases to ensure safety, stability and security of software applications.

Software development and quality assurance face continual pressure: requirements change frequently, releases happen at ever-shorter intervals, and efficient testing strategies must keep up. In this presentation, I demonstrate how Risk-Based Testing (RBT) and Test Automation (TA) can be merged into a comprehensive approach that leverages the best aspects of both, ensuring maximum testing efficiency and focusing efforts on the most critical areas.

• Basics of RBT: Why is it needed and what are the benefits.
• How do RBT and TA benefit from each other: Exploiting synergies to enhance risk coverage and prioritize automation efforts.
• Multi-layered Risk Evaluation: How multiple perspectives enhance overall risk assessment.

This talk targets test managers, QA professionals, developers, and anyone aiming to establish an effective yet resource-friendly testing strategy to direct automation efforts at the most impactful areas.

I would like to present this topic with below agenda:

• Introduction (Importance and Objective of effective test automation)
• Defining Test Automation Effectiveness
• Key Criteria for Evaluating Test Automation
• Common Challenges
• Best Practices for Enhancing Effectiveness

Understanding the key metrics for test automation effectiveness

• Awareness of common challenges and how to overcome them