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-off between privacy and platform lock-in. Users must stay loyal to platforms if a model of adequate utility is to be built around them. This tension is sharpened by emerging regulatory obligations around transparency and user oversight (e.g., GDPR and the EU AI Act). We propose a full-day EICS 2026 workshop that focuses on engineering cross-compatible personalization: a vision for future interactive systems in which users can make their digital self-representations inspectable, editable, and selectively shareable. The workshop brings together researchers and practitioners from interactive systems engineering, HCI, AI/ML, and security/privacy to (1) map the design and engineering space of user-sovereign personalization; (2) derive reusable artifacts for the EICS community, such as a reference architecture, protocol sketches, and a pattern language of interaction techniques; and (3) seed a community around building interoperable toolchains for AI personalization that is not only accurate, but also controllable, auditable, and context-adaptive.
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.
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 personalization may improve the development experience, it raises concerns that generated software could be shaped by attributes of the developer rather than by task requirements alone. Prior work has shown that generative AI can produce biased software artifacts, but little is known about how developer identity can bias generated code. We characterize three dimensions through which inferred developer attributes can influence generated artifacts: interface design, template content, and code structure. First, through controlled experiments on 800 AI-generated websites, we find that age- and gender-related signals produce significant differences across all three dimensions. Second, we conduct an observational study and follow-up interviews with 20 participants who used AI to create a personal website to both examine how personalization impacts software artifacts in practice, and also to understand how programmers perceive the boundary between personalization and bias. Together, our results show that developer attributes can meaningfully influence generated software beyond stated requirements, highlighting a previously underexplored tension between personalization and fairness in AI-assisted programming.
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 exhibit substantial heterogeneity across multiple dimensions: coding style, toolchain preferences, domain-specific expertise, and problem-solving strategies. Failing to account for these individual differences directly compromises both the effectiveness of code intelligence and the likelihood of its adoption. For example, a senior architect and a junior engineer ask: "Describe the authorization module." Without personalized context, the system produces a uniform response—verbose for the expert, incomprehensible for the novice. This gap motivates a fundamental shift: from one-size-fits-all to one-size-fits-one code intelligence. A developer's dynamic in-IDE behaviors—code authoring patterns, navigation pathways, debugging trajectories—implicitly encode a rich representation of their competencies and habits. If captured and interpreted systematically, these signals can enable Personalized Code Intelligence, formalized as: [EQUATION] where P is the developer persona derived from IDE behaviors, injected alongside code context C and instruction ℐ.
Yuhong Liu, Yu Su, Zhipeng Peng et al.· SIGSOFT FSE Companion· 1 citation
The Microsoft 365 (M365) ecosystem hosts thousands of third-party applications that integrate with enterprise tenants via fine-grained OAuth permissions, potentially granting access to sensitive organisational resources such as emails, files, calendars, chats, and user directories. Despite the security implications of these permission grants, the M365 ecosystem has not been systematically studied. We present the first privacy- and security-oriented measurement of M365 third-party applications. By combining public marketplace APIs with automated tenant-side deployment, we crawl over 8,000 applications. We find that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency across official distribution channels. Next, we leverage a topic-aware anomaly detection framework to assess whether requested permissions align with declared functionality. We cluster applications via Neural Topic Modelling and apply unsupervised anomaly detection within each topic to identify deviations from peer permission profiles. LLM-assisted analysis of the most anomalous cases and blind manual inspection reveal a correlation between anomalous permission profiles and the risk associated with the requested permissions. We find that many applications request overly broad tenant-wide scopes (e.g., directory-wide read/write access), violating least-privilege principles and increasing the organisational attack surface. Our pipeline provides tenant administrators with actionable insights by identifying anomalous applications and the permissions that most contribute to their anomalousness. Overall, our findings expose systemic opacity and structural immaturity in the M365 app ecosystem, where permission disclosure is inconsistent and over-privileged access is common.
Vincenzo Longo, Alberto Verna, Nikhil Jha et al.· 0 citations