SUMIT PAL – Independent Consultant

DATA TESTING AND VALIDATION IN DATA ENGINEERING

• Testing is still completely underrated in the data world. Still, almost all data developers shy away from testing.
• Data teams don’t implement tests in the right places and data issues are caught by end-users
• Data Tests not implemented in the right places

This session will show the audience the WHY, WHERE, and HOW to do data testing and validation before working on any data-driven project using AI and ML-infused emerging concepts and tools.

MESUT DURUKAL – Senior QA Automation Engineer, Indeed

THE FUTURE IS TODAY: LEVERAGING AI IN SOFTWARE TESTING

In this presentation, we’ll delve into the use of Machine Learning (ML) in Software Testing, accompanied by practical examples and a case study on Bug Triage. Let’s embrace the future together!

Challenges:

• Testing is labor-intensive, and Agile allows for changing expectations.
• Implementation changes can break tests, and time is limited, necessitating a reduction in manual efforts.

Solutions:

ML can streamline testing in various stages, including test definition, automatic code generation, execution (exploratory testing), maintenance, code review, bug fixing, test case prioritization, and Bug Triage.

Results & Conclusion:

We’ll highlight how ML benefits each stage and discuss the advantages and potential risks of AI in software testing. In summary, this talk addresses the vital issue of AI-based applications in software testing, considering the current prominence of AI in the software industry. We cover a range of AI applications in different testing phases to cater to your specific interests.

LIEZL DUPAGEN – Software Engineer Manager, Deltek Systems (Philippines), Ltd

TESTING TODAY’S APPLICATION – TEST AUTOMATION TOOLS YOU CAN USE

These innovative Automation tools are not just another technological advancement; it’s a catalyst for efficiency, productivity, and continued growth of software testing.

Learn and know the pros and cons of using these Code Based and Low Code Automation Solutions.

KAMILLE NINA AGREGADO – Performance Test Manager, Stratpoint Technologies

ACCELERATE DIGITAL TRANSFORMATION THROUGH PERFORMANCE TESTING

Riding the wave of continuous technology innovations urges businesses to shift to digitalization. As technology reshapes our industry and transforms the way we live and work, the demand for high-performing, responsive, and seamless applications is high.

Survive and thrive in today’s digital world with the help of performance testing by identifying bottlenecks and optimizing application performance to achieve a seamless customer experience. Launch your application in the market and grow your business to newer heights of success.

DMYTRO MYNZIAK – SDET | Automation QA Engineer, GM Consulting

MASTERING TEST AUTOMATION CHALLENGES FOR AUGMENTED REALITY IN NATIVE MOBILE AND WEB APPS

Dive into Real-World AR Testing: Immerse yourself in the realm of AR technology within native mobile and web apps. Confront challenges tied to unique scenarios, regression testing, and innovative web app solutions. Explore how to revolutionize your testing strategies and embrace the power of early-stage automation.

JOEY NYL FLORITA – QA Supervisor/QA Head, Custm

DIGITAL TRANSFORMATION IN QUALITY ASSURANCE – FOLLOWING THE CHANGE IN QA

Today’s digital transformation in Quality Assurance (QA) involves leveraging technology, processes, and data to improve software development and testing efficiency and effectiveness. This can significantly impact various phases of the Software Development Lifecycle (SDLC). Measuring the quality throughout the entire software development and testing lifecycle, from inception to production, is essential for delivering successful and reliable software products.

Since Quality should be a continuous focus at every stage or phase of development, there are some Key Performance Indicators (KPIs) and metrics in QA that should be aligned with the Software Development Life Cycle (SDLC) phases to measure the impact of these changes.

• Inception/Planning Phase
• Design Phase
• Development Phase
• Testing Phase
• Deployment/Release Phase
• Post-Production/Operations Phase
• Feedback and Improvement Phase

There may be other KPIs and metrics to measure quality, but each organization may vary with the KPIs or metrics based on the specific goals and the nature of the software projects.

These KPIs and metrics are important as they help continuously improve in terms of measuring the quality of the projects and also in today’s digital transformation of Quality Assurance processes.

ROMMEL CRUZ – Quality Engineering Architecture Senior Manager, Accenture

GEN AI ON TESTING

With the emergence of Gen AI, what is its impact on software delivery, more focused on software testing?