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

arXiv: https://arxiv.org/abs/2506.12691 · HTML: https://mlsanity.com/paper/2506.12691/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation](https://mlsanity.com/paper/2402.14978/) (2024)
- [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024)
- [Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages](https://mlsanity.com/paper/2310.17526/) (2023)
- [Towards Evaluation Guidelines for Empirical Studies involving LLMs](https://mlsanity.com/paper/2411.07668/) (2024)
- [Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models](https://mlsanity.com/paper/2310.11113/) (2023)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [Can AI Serve as a Substitute for Human Subjects in Software Engineering Research?](https://mlsanity.com/paper/2311.11081/) (2023)
- [Empirical Standards for Software Engineering Research](https://mlsanity.com/paper/2010.03525/) (2020)
- [A Disruptive Research Playbook for Studying Disruptive Innovations](https://mlsanity.com/paper/2402.13329/) (2024)
- [Guiding Principles for Using Mixed Methods Research in Software Engineering](https://mlsanity.com/paper/2404.06011/) (2024)
- [Social Science Theories in Software Engineering Research](https://mlsanity.com/paper/2202.07519/) (2022)

## Built on by (most cited first, 3 of 3)

- [Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence](https://mlsanity.com/paper/2509.23573/) (2025)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers](https://mlsanity.com/paper/2608.27927/) (2026)

## Abstract (arXiv)

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.