Exploring the Evidence-Based Beliefs of LLM-Based Programming Assistants (2407.13900v2)

Abstract: Recent innovations in AI, primarily powered by LLMs, have transformed how programmers develop and maintain software -- leading to new frontiers in software engineering (SE). The advanced capabilities of LLM-based programming assistants to support software development tasks have led to a rise in the adoption of LLMs in SE. However, little is known about the evidenced-based practices, tools and processes verified by research findings, supported and adopted by AI programming assistants. To this end, our work conducts a preliminary evaluation exploring the beliefs of LLM used to support software development tasks. We investigate 17 evidence-based claims posited by empirical SE research across five LLM-based programming assistants. Our findings show that LLM-based programming assistants have ambiguous beliefs regarding research claims and lack credible evidence to support responses. Based on our results, we provide implications for practitioners adopting LLM-based programming assistants in development contexts and shed light on future research directions to enhance the reliability and trustworthiness of LLMs -- aiming to increase awareness and adoption of evidence-based SE research findings in practice.