While we embrace AI in the future, how can we leverage it in our existing framework…can we integrate and move along so that we are not moving to new tools which is a long process
Hi there,
In case you missed the live session, no problem at all! You can check out the recording right here:
Additionally, we’ve got you covered with a detailed session blog:
In my opinion, to prepare for upcoming AI testing openings, QA professionals can focus on learning key AI concepts, programming languages like Python, and exploring machine learning basics. Online courses, workshops, and hands-on projects can help build the necessary skills. Stay informed about industry trends and leverage resources to stay ahead in the evolving field.
Hi…
While Generative AI showcases remarkable capabilities, achieving full autonomy demands ongoing research, development, and ethical considerations. Addressing biases, enhancing context understanding, and incorporating user guidance are integral components of the journey towards creating autonomous.
AI systems that align with human values and societal expectations. Continuous collaboration between AI researchers, developers, and ethicists is essential to navigate these challenges and advance towards the goal of autonomy responsibly.
It was indeed an interactive session i would love to answer this query on behalf of the speaker as I personally attended this session.
Mobile app testing is indeed becoming increasingly crucial as mobile usage continues to rise. With more people relying on mobile devices for various tasks, ensuring the quality and functionality of mobile apps is paramount. However, this does not necessarily mean that mobile app testing will overshadow testing for web-based applications.
Both types of testing are important, and the focus may vary depending on the specific needs and context of each project. Ultimately, a comprehensive testing strategy should consider mobile and web-based applications to deliver the best user experience across all platforms.
I hope this helps 
Hey,
Based on the session, I believe acknowledging bias is an important first step, but it’s not enough to clean the data. To eliminate bias, you need to actively address it through various techniques such as:
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Data Collection: Ensure the data is collected from diverse sources to avoid sampling bias.
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Data Preprocessing: Use techniques like data cleaning, normalization, and outlier detection to reduce bias.
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Feature Selection: Choose features that are relevant and representative of the population, avoiding features that might introduce bias.
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Algorithm Selection: Use algorithms that are less prone to bias or modify existing algorithms to reduce bias.
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Validation and Testing: Validate the models using diverse datasets and testing methodologies to ensure they are not biased.
Ethical considerations are also crucial. It’s important to consider the impact of your data and models on different groups and to ensure fairness and transparency in your processes.
I hope this answers well.
Hey,
This is the major concern of lots of working professionals out there; let me share my thoughts on this query with you.
AI is already automating some QA tasks, but it’s unlikely to completely replace human QA professionals. Human judgment and expertise are still crucial in testing complex systems and ensuring quality.
So, I believe humans are needed if AI is in any picture. It self understood that AI is made by a human and must be trained by a human.
Let me know what you think about this.
This session was impressive as these experts shared their valuable insight and their learnings which is very helpful for all.
To leverage AI in your existing framework without completely changing tools, you can consider the following approaches:
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Integrate AI-powered plugins or libraries: Look for AI-powered plugins or libraries that can be integrated into your existing framework. For example, you could use AI-based tools for test generation, test optimization, or log analysis.
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Use AI for test data generation: AI can be used to generate realistic test data, which can enhance the coverage of your existing tests.
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Implement AI for intelligent test execution: AI algorithms can help prioritize tests based on risk, historical failure data, or code changes, optimizing test execution.
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Apply AI for log analysis and anomaly detection: Use AI to analyze logs and identify anomalies that may indicate bugs or performance issues.
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Explore AI for test result analysis: AI can be used to analyze test results and identify patterns or trends that may help improve testing strategies.
By integrating AI in these ways, you can enhance your existing framework without needing a complete overhaul.
Hope this helps
Hi!
Absolutely get it! To convince managers, focus on showcasing the benefits – emphasize efficiency gains, faster releases, and improved product quality with new tech. Highlight success stories or case studies from similar companies that made the leap.
Offer a phased approach to minimize risks and demonstrate value incrementally. Showcase cost savings in the long run and emphasize the evolving industry standards that necessitate tech updates. Paint a vivid picture of how it streamlines workflows and empowers the team. Lastly, a small pilot project can be a low-risk way to prove the concept.
Good luck!
Hey there,
Based on the panelist’s discussion, this is what the overall pointts i could recall
Testing jobs related to AI typically involve:
- Training Data Quality: Ensuring the quality of data used to train AI models.
- Model Validation: Testing AI models to ensure they meet requirements and perform as expected.
- Bias Detection: Identifying and mitigating bias in AI algorithms.
- Performance Testing: Testing the performance of AI systems under various conditions.
- Ethical Testing: Ensuring AI systems comply with ethical standards and regulations.
- Security Testing: Testing AI systems for vulnerabilities and security risks.
I hope this helps
Hey there,
To ensure data privacy and security when training AI on sensitive data, measures like collecting only necessary data, anonymizing data, encrypting it, controlling access, and complying with regulations are implemented. These steps help protect personal information and prevent unauthorized access.
I hope this answers your query
Hey there,
To manage advancements and deployments of Generative AI technologies, effective regulatory frameworks could include:
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Transparency Rules: Requiring transparency in how Generative AI is developed and used.
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Data Privacy Laws: Enforcing strict data protection regulations for personal data used in training Generative AI.
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Ethical Guidelines: Establishing ethical guidelines for the use of Generative AI to prevent harm.
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Safety Standards: Developing safety standards to minimize risks associated with Generative AI.
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Licensing Requirements: Implementing licensing requirements for organizations using Generative AI to ensure competence and responsibility.
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International Cooperation: Promoting international collaboration to ensure consistent standards for Generative AI regulation.
These frameworks aim to balance innovation with protection for individuals and society.
I hope this answers your query
Hey there,
The assertion that testers will become obsolete as developers can also handle testing duties is not entirely accurate. While it’s true that developers can and should be involved in testing their own code (a practice known as “shift-left testing”), dedicated testers still play a crucial role in ensuring software quality. Here’s why:
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Different Perspectives: Testers often think differently from developers, which can lead to different test scenarios and uncovering different types of bugs.
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Focus on Quality: Testers specialize in ensuring the quality of the software, which can be overlooked when developers are solely focused on building features.
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Independent Evaluation: Testers provide an independent evaluation of the software, which can be more objective than self-testing by developers.
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Specialized Skills: Testers have specialized skills in testing techniques, tools, and methodologies that developers may not possess.
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Efficiency: Having dedicated testers allows developers to focus on building features while testers focus on finding and fixing bugs, leading to a more efficient development process.
While developers can and should be involved in testing, testers still play a vital role in ensuring software quality and are unlikely to become obsolete. Both roles complement each other and are necessary for delivering high-quality software.
I hope this resolved your query
Hello,
I’d like to share with you LambdaTest, one of the leading cloud-based quality assurance (QA) platforms in the industry.
LambdaTest allows you to run automated and manual cross-browser tests across a variety of browsers and operating systems. As a result, your application will perform flawlessly across every user touchpoint.
One of LambdaTest’s key advantages is that it allows QA teams to run automated Selenium (Selenium), Cypress (Cypress), Playwright (Playwright) or Puppeteer (Puppeteer) tests across multiple browser and operating systems.
Integrating LambdaTest with various CI / CD tools streamlines the testing process and reduces the risk of errors.
With LambdaTest, you’ll be able to test in real-time and perform interactive browser compatibility tests. This allows you to catch and solve issues early, increasing productivity and improving the quality of your final product.
Hello,
It’s fascinating to think about all the ways in which AI can change the way we test frameworks, particularly when it comes to platforms like lambdaTest.
AI can dramatically improve testing efficiency and precision by automating test case identification, predicting failure points, optimizing test coverage on the basis of historical data, and more. For example, within lambdaTest, AI can be used to automatically select and prioritize the test cases most likely to reveal new defects based on application changes and historical test results.
This not only accelerates the testing process, but also helps to identify critical issues early.
AI-powered visual testing could also automatically detect visual regression across various browser versions, improving the platform’s ability to deliver seamless user experiences.
By incorporating AI into lambdaTest, we’ll be able to create more resilient, adaptive test frameworks that proactively respond to the ever-changing demands of software evolution.
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As a QA manager, ensuring AI’s health check involves a few key steps. Firstly, define clear metrics for AI performance based on project requirements. Next, establish regular testing protocols to assess these metrics against expected outcomes. Utilize AI monitoring tools to track performance in real time and flag any deviations or anomalies. Lastly, foster a culture of continuous improvement by analyzing testing results and updating health check criteria as needed. This approach helps QA teams confidently ensure AI’s reliability and effectiveness.
In my role as a Quality Improvement Specialist, I find that relying on AI results is beneficial but not a simple plug-and-play process. While AI tools can enhance testing efficiency, contextual understanding and domain expertise are crucial. It’s essential to validate AI outputs within our specific framework, considering the intricacies of our applications. Continuous monitoring, adaptation, and collaboration between AI and human testers ensure the reliability of results, contributing to a robust testing strategy.
In my experience, utilizing AI, especially a private language model trained on confidential medical datasets, has great potential in revolutionizing medical product testing. A robust verification and validation strategy is paramount. I recommend thorough testing against diverse medical cases, continuous monitoring, and collaboration with healthcare professionals for real-world feedback. Rigorous validation, adherence to regulatory standards, and transparent documentation of the AI tool’s performance will ensure its reliability in contributing to safe and effective medical product testing.
Hello,
Indeed, the advent of AI in the tech landscape has ushered in a transformative era, especially in the realm of Quality Assurance (QA). For QA testers, the refusal to adapt and upgrade skills in line with AI advancements is akin to standing still on a moving train. The integration of AI into testing processes not only streamlines and enhances efficiency but also opens up new avenues for innovation in test automation, anomaly detection, and predictive analysis. Embracing AI tools and methodologies is not just about safeguarding one’s career; it’s about being at the forefront of crafting future-proof, resilient, and user-centric software solutions. As we navigate this AI-driven world, the mantra for survival and success in the QA domain is clear: evolve, embrace, and excel. Let’s not view AI as a job taker but as a catalyst for our professional growth and transformation.
Hello! As someone with several years of experience in test automation, I can share insights into concepts similar to Playwright’s autoplay feature within the WebDriverIO ecosystem. While WebDriverIO and Playwright are both powerful tools for browser automation, their approaches and feature sets can differ significantly. As of my last update, WebDriverIO supports similar automation capabilities to Playwright, including handling autoplay scenarios through browser automation scripts. Leveraging WebDriverIO’s extensive API, you can automate interactions with multimedia elements, ensuring autoplay features work seamlessly during tests, akin to what you’d achieve with Playwright.