# Empirical and Sustainability Aspects of Software Engineering Research in the Era of Large Language Models: A Reflection

arXiv: https://arxiv.org/abs/2510.26538 · HTML: https://mlsanity.com/paper/2510.26538/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

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

- [Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance](https://mlsanity.com/paper/2602.08915/) (2026)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape](https://mlsanity.com/paper/2604.11184/) (2026)

## Abstract (arXiv)

Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replicability, and sustainability. In this paper, we invite the research community to reflect on how these challenges are addressed in SE. Our results provide a structured overview of current LLM-based SE research at ICSE, highlighting both encouraging practices and persistent shortcomings. We conclude with recommendations to strengthen benchmarking rigour, improve replicability, and address the financial and environmental costs of LLM-based SE.