It’s been over 70 years since Alan Turing defined what many still consider to be the ultimate test for a computer system — Can a machine exhibit intelligent behavior that is indistinguishable from that of a human? Originally coined the imitation game, the Turing test involves having someone evaluate text conversations between a human and a machine designed to respond like a human. The machine passes the test if the evaluator cannot reliably tell the difference between the human versus machine-generated text. Although the Turing test generally serves as a starting point for discussing AI advances, some question its validity as a test of intelligence. After all, the results do not require the machine to be correct, only for its answers to resemble those of a human.

Whether it’s due to artificial “intelligence” or imitation, we live in an age where machines are capable of generating convincingly realistic content. Generative AI does more than answer questions, it writes articles and poetry, synthesizes human faces and voices, creates music and artwork, and even develops and tests software. But what are the implications of these machine-based imitation games? Are they a glimpse into a future where AI reaches general or super intelligence? Or is it simply a matter of revisiting or redefining the Turing test? Join Tariq King as he leverages a live audience of software testing professionals to probe everything from generative adversarial networks (GANs) to generative pre-trained transformers (GPT). Let’s critically examine the Turing test and more because it’s judgment day — and this time, we are the judges!

Conventional test automation approaches are time-consuming and can produce scripts that are fragile and overly sensitive to change. The rise of AI-driven test automation tools is promising more robust and resilient test scripts that are able to self-heal as the application evolves. But what exactly is this technology all about and how do you get started? Does it require learning new skills and technologies? What tools are immediately available for beginners? Join Tariq King as he introduces you to the world of AI-driven test automation. Learn the fundamentals of AI and ML and how you can apply it to software testing problems. Discover where you can find freely available, open-source tools to support AI-driven test automation. Using a step-by-step approach, Tariq guides you through the basics of what is needed to help you get started with AI-driven testing. No prior programming or AI/ML experience needed!

Delivering test automation in-sprint – before the developers finish writing the code – delivers far more value than test automation delivered later. Delivering in-sprint let’s the developers run all of the tests before they open a pull request, so they find and fix the bugs before QA ever gets the code. This is a win-win-win – it significantly improves developer and QA efficiency and delivers new features faster with fewer bugs, making the product owner and the customers happier too!

• 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.