An analysis of impacts of GenAI and automation tools usage on quality-intelligence 

AI and automation technologies are empowering engineers more than ever before. Organizations are increasingly investing in automation tools to support their employees and enhance productivity. At the same time, they are adopting generative AI (GenAI) tools that assist engineers in reviewing and generating code. These tools contribute to improving the overall quality of work products, particularly in terms of accuracy, readability, and maintainability. Engineers now frequently rely on such tools to generate code, develop test cases, and analyze test results. However, as this reliance grows, it may have implications for the overall quality-intelligence of the system both in the short and long term. In this context, quality-intelligence refers to the ability to perceive, analyze, and evaluate inputs to enhance the quality of a product. The system is defined as the interconnected network of data, tools, and individuals collaboratively working to improve software quality.

This paper presents several experiments conducted by academic and industry experts to analyze the impact of GenAI tool usage on engineers' problem-solving skills and knowledge base. While examining the effects on quality-intelligence, the paper also proposes strategies to mitigate potential negative consequences, thereby maintaining healthy, safe, and continuously improving software products.

Paper | Presentation

Manju Bisht

https://www.linkedin.com/in/manju-bisht-asu/