Jun 2026· Proceedings of the ACM on Software Engineering· Vol 3, pp. 3651 - 3674· 0 citations· 85 references
TL;DR
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.
Abstract
Collaborative platforms such as Google Workspace, Microsoft Teams, and Zoom increasingly rely on third-party applications (referred to as plugins) to extend their core functionalities, with AI-assisted plugins emerging as a key driver of productivity. Despite their popularity and rapid adoption, little is known about the characteristics of the marketplace, the potential security and privacy risks that concern users, and the compliance of plugins with AI ethics guidelines. In this paper, we present the first large-scale, cross-platform study of plugins from five major web application marketplaces, covering domains from office productivity to software development. We systematically examine the distribution characteristics of current plugins, analyze users’ concerns, and assess their compliance with emerging AI regulations. Our findings indicate that (i) the current marketplaces exhibit an uneven distribution of functionality and installations, (ii) AI-assisted plugins face a range of emerging issues that negatively impact user experience, and (iii) a significant proportion of plugins fail to comply with established AI ethics principles. Our work highlights the need for strigent policies and security auditing to maintain quality of AI-assisted plugins.
AI-driven services continuously infer “models-of-users” to personalize content, automate decisions, and optimize interaction. Yet, most user models remain opaque, fragmented across platforms, and hard to correct, transfer, or revoke. As a result, personalisation in AI-driven services is commonly experienced as a trade-...
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...
Adam Retter· Balisage Series on Markup Te...· 0 citations
The goal of this study is to understand practitioners'decision making process and security considerations in selecting and integrating AI components through an exploratory semi-structured interview study, and distill the findings into actionable recommendations for AI adopters, model providers, and researchers.
Mahzabin Tamanna, Elizabeth Lin, Sparsha Gowda et al.· 0 citations
Generative AI is increasingly permeating software engineering, enabling developers to generate functions, files, and even entire applications from natural language specifications. AI systems are also becoming more personalized, adapting outputs based on inferred user characteristics and interaction history. While perso...
The rapid advancement of LLMs has opened new opportunities in automated software engineering, driving progress in code understanding, agent-based workflows, and productivity tools. However, existing code intelligence systems have largely sidelined the end-users they aim to serve—the developers themselves. Developers ex...
Yuhong Liu, Yu Su, Zhipeng Peng et al.· SIGSOFT FSE Companion· 1 citation
This work presents the first privacy- and security-oriented measurement of M365 third-party applications, and finds that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency across official distribution channels.
Vincenzo Longo, Alberto Verna, Nikhil Jha et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.