Ways of Applying Artificial Intelligence in Software Engineering
This provides a framework for companies to assess risks and strategies in applying AI in software engineering, but it is incremental as it builds on existing work to address a specific classification gap.
The paper tackles the lack of methods to classify AI applications in software systems and analyze associated risks by introducing the AI-SEAL taxonomy, which categorizes applications based on AI point of application, technology type, and automation level, and demonstrates its usefulness by classifying 15 papers from the RAISE workshop.
As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.