Towards an Adoption Framework to Foster Trust in AI-Assisted Software Engineering

The adoption of artificial intelligence (AI) tools, specifically those based on large language models (LLMs), has quickly led to turbulent changes in software engineering practice. However, despite their potential, AI-based tools are often thrust into the development process with little consideration towards establishing trust, a key factor in the adoption of new tooling. With this work, we address this trust gap by focusing on the needs of software engineers to facilitate a trust-based, synergistic adoption framework in AI-assisted software engineering (AI4SE). We aim to identify the key factors influencing trust in AI4SE and to design a practical adoption framework that covers the concrete steps and best practices for a trust-based integration of AI4SE. Finally, we evaluate the designed framework by applying it to a real-world use case with a professional software development team. By treating trust as a core design goal, this research provides practical guidance to improve the developer experience and facilitate a seamless adoption of AI4SE tooling.