Bram Adams | Search arXiv

arXiv2026

Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality

Large Language Models (LLMs) are increasingly used by developers for conversational coding assistance, including code generation, technical questions, and iterative problem solving. However, limited empirical evidence exists on the topics developers discuss with LLMs, how interaction patterns vary across tasks, and how the quality of generated code changes across conversational turns. To address this, we construct CodeChat, a large-scale dataset derived from WildChat. CodeChat contains 587,568 real-world developer-LLM conversations and 1.7 million code snippets across more than 20 programming languages. We identify the topics developers raise and examine how their engagement varies across topics. We then assess the quality of generated code across five programming languages (i.e., Python, JavaScript, C++, Java, and C#) and analyze how quality issues evolve across conversational turns. We find that developers most frequently seek help with web design (9.6% of conversations) and machine learning (8.7%), and that conversations often grow long because developers repeatedly shift use cases. LLM-generated code exhibits prevalent, language-specific issues whose prevalence does not consistently decrease in interactions lasting at least five turns. Among successful syntax-error resolutions, prompts that explicitly identify the error and request a fix are the most frequently observed. Our findings suggest that conversational assistants should track evolving developer intent and monitor code quality across turns.

cs.SE↗

arXiv2026

On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces

AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly extended through plugin marketplaces, yet the structure, maintenance, and co-evolution dynamics of these emerging repositories remain empirically unexplored. Unlike traditional software packages that deliver functionality through source code, agent plugins deliver functionality through a combination of natural-language instruction files, scripts, and configuration files, raising the question of whether these plugins are maintained artifacts that co-evolve across components, or one-off artifacts that developers write once and do not need to revisit. To study the maintenance and co-evolution of agent plugins, we conduct an empirical study of 1,926 repositories hosting Claude Code plugin marketplaces, analyzing 8,351 plugins and 77,773 commits across 2,018 marketplaces. We find that the marketplace is expanding rapidly, plugin-touching commit activity growing 8.8x over six months after the October 2025 launch, and plugins targeting Software Engineering tasks accounting for 61.3% of all plugins. Plugin development is predominantly feature-driven, with feature commits occurring at more than twice the rate of conventional open-source software (OSS) (39.6% vs. 17.2%). Claude co-authors 34.9% of all commits, and four commit types (docs, perf, style, and refactor) carry substantially different meanings in plugin repositories than in traditional software. Most component types evolve independently, but within skills directories, natural-language instruction files and implementation scripts co-evolve at above-chance rates, with 78% of co-changes being functionally coupled, representing a new class of maintenance dependency not observed in traditional software engineering.

cs.SE↗