What is the ROI of investing in AI-powered testing?
What are the ethical considerations when using AI in testing, especially regarding bias in test data and the potential impact on end-users?
How can it be more practical in real life???
What sort of adaptive learning are AI models that are used in testing being trained on that will make them more powerful, ubiquitous - or always-on and everywhere needed?
Vikash: I’ve seen AI update tests by finding elements that have small textual changes. Like “Add To Cart” changes to “Add To My Cart” and the AI updates the new object reducing maintenance.
Are there certain types of testing that you believe AI will never be able to handle effectively? If so, why?
How ai can fasten automation?
Should testers expect a compromise in capability or speed (or another metric) in omnipresent AI-powered testing?
How can QE skill themselves, can you talk about one practical scenario?
Strictly from the testing perspective, given the omnipresent potential, are we closer to a kind of AI capability approaching AGI?
Could you please bring some examples or best practices?
Are there any tools to test AI?
Hello!
As I attended this session, I’d like to share that AI in testing doesn’t necessarily reduce the need for human testers. Instead, it transforms their roles, allowing them to focus on strategic tasks such as test design and improving overall quality, while AI handles repetitive and time-consuming testing activities.
I hope this clarifies your question!
Hi there!
From the insights shared in the session, AI bridges the gap between code development and quality assurance by automating testing processes and providing real-time feedback. It enables better communication between teams and helps identify defects earlier in the development cycle, ensuring a smoother integration of quality throughout the process.
Hope this helps!
Hello!
Based on the session, challenges in implementing AI into testing workflows include data quality issues, resistance to change, and the need for skilled personnel. These can be mitigated by investing in training, ensuring high-quality data, and gradually integrating AI tools while fostering a culture of collaboration and innovation.
I hope this answers your question!
Hi!
As discussed in the session, AI helps create intelligent and adaptable test cases by analyzing historical data and application behavior. It can dynamically adjust tests based on changes in the application, ensuring that they remain relevant and effective in complex environments.
Hope this clarifies your query!
Hello!
I would like to share my insights from from session, AI maintains continuous quality by automating testing at each stage of the development lifecycle. It ensures consistent validation through continuous integration and delivery processes, providing timely feedback and allowing teams to address issues proactively.
I hope this helps you understand AI’s role!
Hi there!
I would like to share my opinion based on the session, one limitation of AI in testing is its reliance on quality data. AI can struggle with biased or incomplete datasets, leading to inaccurate test results. Additionally, AI may not fully replicate human intuition and creativity in certain testing scenarios.
Hope this provides clarity!
Hello!
From what I gathered in the session, AI enhances testing by automating repetitive tasks, improving defect detection, and enabling predictive analytics. This leads to faster test cycles and higher overall quality, allowing teams to focus on more complex testing challenges.
Hope this answers your question!
Hi!
From experience in automation testing, I would suggest that tools like Test.ai and Applitools are emerging as game changers in QA. They leverage AI for intelligent test automation and visual validation, enabling teams to streamline their testing processes and enhance accuracy.
Hope this helps!