· Balisage Series on Markup Technologies· 0 citations· 8 references
Abstract
The recent meteoric rise of LLMs (Large Language Models) and associated tools was largely unexpected and surprising to most. The rapid ascent of this technology has caught many software developers unawares, leaving them suddenly somewhat ignorant, and arguably under-skilled.
LLMs, whilst still advancing, have recently demonstrated impressive capabilities in their ability to assist software developers in their day-to-day tasks (e.g., coding new features, and locating and fixing issues). However, the use and adoption of LLMs presents many larger challenges for society as a whole; many of which are not in themselves technical concerns.
This paper examines the current and perceived impact of this technology in the context of Open Source. We identify several social, economic, environmental, political, legal, and technical concerns regarding the use of LLMs in Open Source projects.
We contribute guidance around defining an AI Policy for Open Source projects. We further offer an AI Policy Score Card to assist projects in clearly defining and declaring how they wish to work with AI or not.
A large-scale empirical study of mainstream open-source agent frameworks from an engineering perspective, providing empirical evidence linking framework design choices to engineering risks and highlighting the need for stronger guidance and support in agent framework development.
Yibo Zhai, Junjun Si, Yan Wang et al.· SIGSOFT FSE Companion· 0 citations
An empirical study of configuration prompt files in Cursor, a widely used AI-assisted code editor, shows that .cursorrules files emerged rapidly from mid-2024 and shows that there is a continuity of themes and topics between the now-legacy .cursorrules files and the current standard .mdc files.
Shuang Sun, Jafar Akhoundali, Arina Kudriavtseva et al.· Proceedings of the 21st Inte...· 1 citation· ⚡1
AI-generated C++ code has a distinct quality profile, showing higher rates of interface and coupling burdens, copy and allocation overheads, and a reliance on explicit loops over optimized standard APIs, which translates into tangible downstream costs, including increased review effort and a 5-8% increase in compute resource consumption.
Michael Tran, Fred Lewis, Kun Yang et al.· 1 citation
The first large-scale, cross-platform study of plugins from five major web application marketplaces, covering domains from office productivity to software development, indicates that AI-assisted plugins face a range of emerging issues that negatively impact user experience and fail to comply with established AI ethics principles.
Liuhuo Wan, Zicong Liu, Chuan Yan et al.· Proceedings of the ACM on So...· 0 citations
This tutorial provides a comprehensive and up-to-date overview of LLM mechanism discovery, validation, and editing, and surveys mechanistic editing techniques that leverage MI insights to modify behavior at varying granularity.
Yinhan He, Wendy Zheng, Tianyi Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
DevEx Metrics Compass is a public, open-source web app built on a structured analysis of that landscape across more than 50 engineering organizations, and what the dataset reveals about how DevEx is measured today and where the gaps lie is shared.
André N. Meyer, Patrick Meyer, Gail C. Murphy et al.· Queue· 0 citations