Dominik Dary's Blog

Apr 23, 2023 - 3 minute read - software analytics ai-driven testing model-based-testing conferences test automation

Blogmark AI Testing

This post is a collection of interesting blogs and findings in the internet about quality engineering, software analytics and other software engineering (leadership) related topics.

Using Transactional Coverage for Detecting Testing Gaps

In his blog post, Baubak Gandomi explores the concept of transactional coverage as a functional approach to detecting test coverage gaps. He discusses the challenges of traditional code coverage analysis and explains how transactional coverage can provide more accurate insights into the effectiveness of testing efforts. The post is thought-provoking and offers a fresh perspective on testing methodologies, making it a must-read for software developers and quality assurance professionals alike.

4 Autonomous AI Agents you need to know

In her blog post, Sophia Yang introduces four different types of autonomous AI agents that are being used in various industries today. She explores each agent’s unique characteristics, such as chatbots that can handle customer service inquiries or autonomous drones that can perform search and rescue missions. The post is a fascinating read that offers insights into the cutting-edge technologies that are shaping our world, making it a must-read for anyone interested in the field of AI.

Explainable AI

Recently I listened to the Neuland podcast Artificial Intelligence - A Question of Trust. It was about artificial intelligence and how its pattern recognition has great potential - for example in the early detection of diseases. But in order to trust an AI, it must be comprehensible how it arrived at its proposed diagnosis. “Explainable AI” is the name of the concept and can be achieved, for example, by marking the relevant data in the input (heat map), which is also relevant for testing AI-based systems.

Risk Based Testing

In this blog post, Hans Schaefer discusses the concept of risk-based testing and its different aspects. The post begins by explaining the importance of risk-based testing in ensuring the quality of software and reducing costs associated with testing. The author then goes on to explain the different stages of risk-based testing, including risk identification, risk analysis, risk evaluation, and risk mitigation. The author also explains the importance of involving stakeholders in the risk-based testing process and how this can help ensure that the software meets the needs of its intended users.

Finally, the post highlights the benefits of risk-based testing, including reduced testing costs, improved software quality, and increased customer satisfaction.

Demand for QA will soon explode

Edoardo Turelli has written an interesting post. He wrote that as the demand for AI-generated code increases, the need for rigorous testing and validation through quality engineering will become crucial. QA methodologies will need to adapt to the new landscape, and QA professionals may shift to higher-level responsibilities such as monitoring and validating go-live gating, assessing reliability and security, and ensuring compliance.

AI Learning Resources

Here’s a list from Misha of 24 top AI Learning resources to get up to speed (for free).

ChatGPT Local Machine

In his blog post, Martin Nilsson shares his experience of running ChatGPT-style LLM on a local machine for sensitive data. He discusses the benefits and challenges of this approach and provides a step-by-step guide on how to set it up. The post is informative and practical, providing useful insights for anyone interested in using ChatGPT-style LLM on a local machine for software testing.