I did a talk today, and published slides. I encouraged actively seeking uses to learn, rather than seeking the one tool that does the thing for you.
https://speakerdeck.com/maaretp/habit-of-ai-in-quality-engineering
Someone from my audience provided an AI-generated example of exactly that one-up attitude to your use of time and speech to text, test to summarized my messages with a basic approved chatbot genAI.
Overlayed in red things where either I or AI missed the mark on the message.
- Introduction to AI in Quality Engineering:
- The document begins with an introduction to the speaker's experiences with AI in quality engineering, highlighting the relevance of AI tools in improving testing processes.
- The speaker shares their initial encounter with GitHub Copilot in 2021, emphasizing its potential to assist in coding and testing tasks.
- Job Interview Experience:
- The speaker recounts a job interview where they used GitHub Copilot during a pair programming session. This experience demonstrated how AI could assist in testing and coding tasks.
- The interview involved converting integers to English words, and the speaker used GitHub Copilot to generate multiple solutions, showcasing the tool's capabilities and limitations.
- Evolution of GitHub Copilot:
- The document details the evolution of GitHub Copilot from its early access days to its current state in 2025. Initially, the tool had limitations, but it has since seen significant improvements and new features.
- The speaker highlights the introduction of features like multi-file context, chat interfaces, and agent-type functionalities, which have enhanced the tool's usability and effectiveness.
- Role of AI in Testing:
- AI is portrayed as a valuable pair programmer and testing assistant, capable of generating code and test cases. However, human oversight and decision-making remain crucial to ensure quality and accuracy.
- The speaker emphasizes the importance of evaluating AI-generated outputs and making informed decisions based on the context and requirements of the task at hand.
- Building AI Habits:
- The document discusses the importance of integrating AI tools into daily tasks to build effective habits. The speaker encourages using AI tools like GitHub Copilot and Edge Copilot to enhance productivity and learning.
- Practical advice is given on how to start using AI tools in everyday work, such as seeking second opinions, refining prompts, and continuously learning from AI interactions.
- Challenges and Considerations:
- The speaker addresses the challenges of balancing automation with manual testing, noting that while AI can assist in many tasks, it is essential to maintain a human touch to ensure comprehensive testing.
- Considerations are given to the quality and accuracy of AI-generated outputs, emphasizing the need for thorough evaluation and validation.
- Future of AI in Quality Engineering:
- The document concludes with a forward-looking perspective on the potential of AI to transform testing practices. The speaker envisions a future where AI tools are seamlessly integrated into quality engineering processes.
- Continuous learning and adaptation are highlighted as key factors in leveraging AI effectively, with an emphasis on staying updated with new features and advancements in AI technology.
The summary layers a tone that isn't mine. The tone of *focusing on today*, considering learning a journey, and the conclusion that for anything and everything we do, we are either learning or contributing. It's that learning that saves percentages of time to allow us to do more, not the autogeneration of contributions. Learning is curation of AI-contributions, deciding on the reigns.
