Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research

TL;DR

This paper discusses how Large Language Models are transforming software engineering research by enhancing capabilities, posing challenges, and emphasizing the need for human oversight and ethical considerations in integrating LLMs into research practices.

Contribution

It provides a theoretical analysis of LLMs' impact on SE research using McLuhan's Tetrad, highlighting opportunities, risks, and the importance of human-centered approaches.

Findings

LLMs accelerate research ideation and automation.

Traditional research practices may become obsolete.

Risks include over-reliance and ethical concerns.

Abstract

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.