  Recent innovations in artificial intelligence (AI), primarily powered by
large language models (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 and behaviors 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, lack
credible evidence to support responses, and are incapable of adopting practices
demonstrated by empirical SE research to support development tasks. 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.
