Day 4: Watch the AMA on Artificial Intelligence in Testing and share your key takeaway

Key Takeaways:

• Context in AI: Context is critical for AI tools. Without context, AI can give incorrect or unreliable results. Tools like Postbot for Postman work well because they understand the context of API testing.
• Security and Confidentiality: It’s important to ensure data security and confidentiality when sharing information with AI tools. Trusting vendors and understanding their terms of service is crucial.
• The Future of Testing: AI could change the future of testing by automating routine tasks, but human testers are still needed for critical thinking and creativity. Testers might focus more on analysis, usability, accessibility, and observability.
• Ethics and Bias: Testing for biases in AI models is essential, as biases in training data can lead to discriminatory outcomes. Testers play a vital role in ensuring AI systems are ethical.
• Human Element in Testing: While AI can automate tasks, the human touch is still necessary for tasks requiring intuition, creativity, and complex analysis.
• AI Tools for Testing: Several AI tools and platforms were mentioned, such as Hugging Face, Report Portal, and Postbot, which are useful for various testing tasks.
• Learning and Upskilling: AI can help junior testers understand testing terminology and gain insights into testing strategies. It’s also useful for exploring new testing tools and approaches.
• Observability and Data Drift: Monitoring AI performance is crucial to ensure it adapts to changing environments and does not become outdated or unreliable due to data drift.

This session highlighted the balance between leveraging AI for efficiency and retaining the unique human skills that add value to testing, emphasizing the importance of context, security, and ethical considerations.