Join Don in this engaging session as we delve into the intricate world of Artificial Intelligence (AI). Don, our expert speaker, will explore the multiple areas of AI research. We’ll discuss how various AI paradigms differ, and why treating them equally can be problematic. We’ll also discuss the impact AI’s role in testing and beyond. Prepare to be inspired by specific examples showcasing AI’s potential.

While many companies are adopting the concept of shifting QA left, only a few are truly embracing the full extent of this approach. Some organizations believe they have successfully “shifted left” but fail to recognize the potential for even greater speed and efficiency in delivering value to customers.

• How to implement Specification by Example/BDD as a development methodology, not as a tool.
• If you are writing test scripts you have already failed.
• How to write – and automate – requirements.
• Zero test cases, zero test scripts, and lots of automation is the right way to go.
• How to reuse your automation across interfaces – use at least half of the automation code for the web, the API, and the mobile apps.
• Cut the cost of maintaining your automation by 50% to 80%.

In this session, you’ll learn how to save 95% of your time on test automation by leveraging Generative AI breakthroughs and moving 90%-100% of the work to the product management organization. We’ll cover how to improve SDLC to allow you to move up to 30% faster with up to 90% fewer bugs in production, and how Generative AI can be helpful to eliminate 98% of the effort on maintaining end-to-end tests. This will be followed up by a live demo of the Generative-AI features of testRigor.

Time Savings: The session highlights how well-written prompts for Generative AI can create modular and dynamic test steps.

Enhancing the SDLC: Generative AI enables faster development cycles by creating more tests in shorter amounts of time.

Shift in Responsibility: Replacing strict code structures with plain English statements results in moving 90%-100% of the test automation workload from Software Developers Engineers in Test (SDETs) to less technical manual testers.

Reduced Maintenance Effort: The effort to maintain page object classes is reduced with self-healing objects. Elements that change properties and even classes can still be accurately located. Furthermore, attendees will learn the adoption of a data-qa, or Data-* attribute, takes maintenance down to its lowest level.

Faster Suite Execution: Running more tests in parallel reduces Defect Detection Percentage, This improves the overall quality of software by finding more defects before they reach production, directly benefiting end-users.

In this talk, we will delve into the transformative journey of integrating GenAI into the core of our testing and development processes. This integration has not only enhanced our productivity by 15% but also yielded a 20% time savings and a significant cost reduction per test case. Want to know how? We’ll explore the strategic implementation of GenAI, overcoming security challenges, leveraging diverse (LLMs), and the meticulous design of prompts that culminated in a prompt library. Our tailored extensions for the code editors and corporate chats exemplify the seamless fusion of AI with everyday tools, facilitating a more intuitive and efficient workflow. This real-life application case study will also touch upon how GenAI adoption has enabled us to decrease the seniority index, democratizing the testing process, and allowing team members at various levels to contribute more effectively. Join us to gain insights into how we harnessed the power of GenAI to revolutionize our approach to quality assurance and project management, setting new benchmarks in operational excellence within the industry.

The words “quality” and “testing” are broad and loosely used words when developers and testers are testing an application or a business process flow. We can attain approval in functional and performance testing but if it doesn’t gain a positive end user adoption then what was gained? This session will dive into the different aspects of testing and what quality really means when looked at from different angles and the adoption of testing it from an End User’s perspective.

In this presentation, “Advanced Test Leadership in Digital QA Leveraging AI: A Comprehensive Framework,” we will explore the transformative role of AI in digital quality assurance (QA) and how advanced test leadership is crucial in navigating the complexities of today’s digital landscape.

We’ll begin with an overview of the evolution of QA, tracing its journey from traditional methods to modern digital approaches, and highlight how AI is revolutionizing these practices. Next, we’ll delve into the common challenges faced in digital QA, such as scalability, complexity, and speed, and discuss how AI can effectively address these issues.

Central to the presentation is a proprietary framework for advanced test leadership, comprising strategic planning, test automation, CI/CD integration, risk-based testing, and more, leading up to Governance. We’ll explore each component, showing how AI enhances these processes through real-world case studies and examples.

Additionally, we’ll cover the specialized area of digital QA for AI applications, discussing how a blend of traditional and intelligent QA frameworks can validate AI use cases, with a focus on governance, risk management, and compliance.

The session will also highlight key leadership and management strategies needed to lead digital QA teams effectively, fostering a culture of continuous improvement and innovation, especially in the world enabled by AI. Practical implementation tips and tools will be provided, ensuring participants leave with actionable insights.

Finally, we’ll look ahead at emerging trends in digital QA and AI, preparing attendees to stay ahead in this rapidly evolving field. The talk will conclude with a Q&A session, offering the audience an opportunity to engage further with the material presented.

• Understanding the Evolution of QA: Gain insights into how digital QA has evolved from traditional methods and the pivotal role AI plays in modernizing QA practices.
• Navigating Digital QA Challenges: Learn about the common challenges faced in digital QA, such as scalability, complexity, and speed, and discover how AI can effectively address these obstacles.
• Proprietary Framework for Test Leadership: Explore a comprehensive framework designed for advanced test leadership in digital QA, covering strategic planning, AI-driven automation, CI/CD integration, risk-based testing, and more.
• AI’s Role in Enhancing QA: Understand how AI can be leveraged across various components of the QA process to improve efficiency, accuracy, and coverage, supported by real-world case studies.
• Specialized QA for AI Applications: Discover the importance of integrating traditional and intelligent QA frameworks to validate AI applications, with a focus on governance, risk management, and compliance.
• Leadership Strategies for Digital QA: Identify essential leadership qualities and strategies needed to manage and lead digital QA teams, promoting a culture of continuous improvement and innovation.
• Practical Implementation Tips: Receive actionable advice on how to implement the advanced test leadership framework in your organization, including tools, technologies, and best practices.
• Future Trends and Preparation: Stay ahead of emerging trends in digital QA and AI, preparing yourself and your organization for future developments in the field.

These takeaways will equip you with the knowledge and tools needed to lead your QA teams effectively in the digital age, leveraging AI to overcome challenges and drive continuous improvement.

In an era where mobile applications are pivotal to business success, ensuring their quality and performance is crucial. This session will delve into the realm of Mobile Application Automated Testing, exploring state-of-the-art techniques and tools that streamline the testing process. Attendees will learn about the latest frameworks, best practices, and strategies for implementing robust automated testing solutions. The discussion will cover key aspects such as cross-platform testing, handling device fragmentation, integrating with CI/CD pipelines, and leveraging cloud-based testing services. Participants will gain insights into improving test coverage, reducing time-to-market, and enhancing overall app quality, ultimately driving better user experiences and business outcomes.

• Understanding of modern tools and frameworks for automating mobile app testing, including Appium and Espresso.
•  Best practices for setting up and maintaining a robust automated testing suite for mobile applications.
•  Strategies for addressing cross-platform testing challenges and handling device fragmentation effectively using tech like Browser stack and AWS Device farm.
• Insights into integrating automated testing with Continuous Integration/Continuous Deployment (CI/CD) pipelines to ensure seamless delivery.
• Techniques for leveraging cloud-based testing services like Browser stack and AWS Device farm to expand test coverage and optimize resource utilization.
• Methods to enhance test coverage, reliability, and maintainability while reducing testing time and costs.
• Tips for identifying and mitigating common pitfalls in mobile application automated testing.
• Real-world examples and case studies demonstrating successful implementation and outcomes of automated testing in (Android and IOS) mobile app projects.

In a regulated industry (healthcare insurance), implementing cutting edge tech takes time. Usually the cutting edge is quite dull – I.E. the tech is confirmed non-data-destructive, application compatible, and industry proven – before Sr. Leadership allows its implementation into the enterprise. During the six to 18 months while this goes on, you gotta keep the lights on. How does QA/QE Leadership continue to move projects forward while shortening POC development and implementation times that so often increase internal costs and lengthen the product development life cycle? Here are five (5) effective ways to insure on time testing while implementing new testing technologies in your STLC.

• Knowledge of Executive Order 13859 – Access to Federal Data and Models for AI R&D
• Knowledge of Executive Order 14110 – Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
• How these EOs affect Software Development and Testing in the “Real World”
• How to Identify & Implement where AI could/should be Implemented First in your SDLC/STLC
• Five Effective Ways to Insure on Time Testing while Implementing New Testing Tech – like AI – in your STLC

In this presentation, we will discuss an innovative software test data management solution that enables teams to safely leverage production data in lower test environments. By securely querying non-sensitive data elements and generating test-safe versions of sensitive data, this approach ensures realistic testing without compromising security. Learn how this solution helps maintain data integrity and compliance while optimizing the testing process.

• Safe Data Access: Learn how to securely access and query production data while ensuring sensitive information is protected.
• Test Environment Realism: Understand the process of integrating non-sensitive production data with dummy/test versions of sensitive elements to create realistic test scenarios.
• Efficiency in Testing: Discover how this solution streamlines testing workflows by allowing test teams to validate real production records in a safe, lower-environment context.
• Compliance and Security: Gain insights into maintaining compliance with data privacy regulations by using dummy data generation for sensitive elements.
• Improved Test Outcomes: Explore how this approach leads to more accurate and relevant testing, helping teams deliver higher quality products faster.

Problem Statement: Ability to test production data/orders in lower environments.

Client is needing to analyze and test their production defects/bugs without having to access the patient’s private production data. Having the ability to analyze production defects and bugs to test in lower environments allows the client to pinpoint actual issues without security and data risks. It allows them to have these records at scale for test coverage and non-functional (load/performance) testing capabilities also.

High-Level Solution: An internal web application that fetches and stores a patient’s order number prescription (Rx) and other medical data (scans, diagrams, etc.) from production, but not their sensitive identifiable patient data on the order number. The application will add de-identified “dummy” test data for a patient for that order, so the order in test will mirror the production record without the unneeded identifiable patient data.

Detail Solution: There is a web application in the lower environment that queries the production data via a record identifier with a secure API that has proper access between environments. The API requests the two non-sensitive data elements from the production id query, and the response is the data from those two non-sensitive data elements. The API then creates a dummy/test version of the third sensitive/identifiable data, and concatenates all three elements (2 non-sensitive production data elements plus the third dummy-data sensitive record) into the test web application.

Dependencies: The main dependencies are updating security access policies for the solution to be able to access and fetch production data. There will need to be proper security protocols and controls in place for the solution to work properly including restrictive use/access and applying a governance structure within the organization. Also, the business and regulatory workflow has to allow that not all elements of a production record are sensitive and can be moved into test environments without issue.

Usage:

The test users can now test the

This presentation is about Cypress and utilizing a Cypress framework for more than just UI tests. This tool can be used for performance benchmarking, accessibility, API testing and more!

I would love to share that Cypress can be used for many facets of testing, not just UI smoke testing. I want to share my passion for the tool with other testers in the community who may not realize all that a Cypress framework can offer in terms of a more wholistic test plan that involves API, accessibility and performance testing.

Join us for an insightful session on enhancing your data quality journey using PySpark, a powerful tool for big data processing.

This presentation will cover practical tips and best practices to help you effectively profile, clean, and validate your data. Learn how to automate quality checks and integrate PySpark with Pytest and service providers such as AWS, Azure Databricks, and Snowflake, ensuring a robust framework for maintaining data integrity. We’ll also discuss more about Setting up Effective and Scalable Test Automation Environments to accelerate your data quality journey to ensure high quality and meaningful data. Whether you’re a data quality engineer, analyst, or business leader, this session will provide actionable insights to accelerate your data quality journey and drive better decision-making.

In today’s data-driven world, ensuring high data quality is crucial for making informed business decisions.

Accelerating Data Quality with PySpark

Real-world data Migration and Modernization Scenarios

• Explore practical examples of data migration projects across various industries. Highlight challenges faced and solutions implemented to enhance data quality during modernization efforts, setting the stage for why robust data management is essential.

Comprehensive Overview of Data Management Flows

• Provides a clear framework for effective data management encompassing data profiling, cataloging, security, and governance. Emphasize how each component plays a critical role in maintaining data quality and ensuring compliance throughout the data lifecycle.

In-depth exploration of Data Validation Dimensions

• Dive deep into key data quality dimensions—Timeliness, Uniqueness, Integrity, Completeness, and Consistency. Use case studies to illustrate how organizations can implement rigorous validation processes, ensuring their data remains reliable and actionable.

Adapting the PySpark Accelerator Test Framework

• Actionable insights on utilizing the PySpark accelerator test framework for data validation and migration. Discuss how integrating Python and Pytest can streamline testing processes, making it easier to adapt and extend the framework for various project needs.

Accelerating Your Data Quality Journey with Reusable Strategies

• Practical tips for enhancing efficiency through reusable helper classes and conditional assertions. Discuss techniques for faster setup, enabling attendees to implement solutions across multiple environments with ease.

Extending Your Framework Across Multiple Cloud Data Sources

• Discuss strategies for integrating your data quality framework with major cloud platforms like AWS, Azure Databricks, and Snowflake. Illustrates how this flexibility supports a broader range of data sources, ensuring consistent quality checks regardless of infrastructure.

Leveraging Generative AI in Your PySpark Test Framework

• Introduces the concept of incorporating Generative AI into your PySpark testing framework. Insights into how AI can optimize testing processes, enhance efficiency, and provide innovative solutions for complex data quality challenges, ultimately accelerating results.

Karthikeyan is a seasoned technology leader with over 15 years of industry experience, specializing in quality engineering, cloud computing, release management, and agile project delivery. With extensive expertise across various domains, Karthikeyan is passionate about architecting and implementing automation frameworks and applying AI/ML models to test automation.

Sivasakthivel Ramamoorthy is the Director of Client Services Automation at CVS Health. Sivasakthivel is a healthcare automation leader and business strategist with more than 17 years of experience in setting up and managing large automation portfolios. Sivasakthivel played an instrumental role in modernizing healthcare pharmaceutical and plan benefit management automation.

Dedicated and motivated software development professional with over 15 years of experience in an agile environment looking to provide leadership in the development of web and mobile application at an enterprise level and set the technical vision and strategy.

Nishanth Sridharan, the Cloud Portfolio Leader and Senior Director at Capgemini, has decades of experience in leading and delivering digital transformation solutions for various clients across industries. With a commitment to creating value and impacting technology, his expertise spans Software Delivery, Cloud, DevSecOps, Automation, Agile, Enterprise Architecture, Application Modernization, Site Reliability Engineering (SRE), and Generative AI. He has a strong passion for technology and innovation.