The Perfection Paradox: From Architect to Curator in AI-Assisted API Design

Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals (AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly "perfect." We characterize this as a "Perfection Paradox" -- where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer's role from the "drafter" of specifications to the "curator" of AI-generated patterns.

From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering

As Large Language Models (LLMs) evolve from code generators into collaborative partners for software engineers, our methods for evaluation are lagging. Current benchmarks, focused on code correctness, fail to capture the nuanced, interactive behaviors essential for successful human-AI partnership. To bridge this evaluation gap, this paper makes two core contributions. First, we present a foundational taxonomy of desirable agent behaviors for enterprise software engineering, derived from an analysis of 91 sets of user-defined agent rules. This taxonomy defines four key expectations of agent behavior: Adhere to Standards and Processes, Ensure Code Quality and Reliability, Solving Problems Effectively, and Collaborating with the User. Second, recognizing that these expectations are not static, we introduce the Context-Adaptive Behavior (CAB) Framework. This emerging framework reveals how behavioral expectations shift along two empirically-derived axes: the Time Horizon (from immediate needs to future ideals), established through interviews with 15 expert engineers, and the Type of Work (from enterprise production to rapid prototyping, for example), identified through a prompt analysis of a prototyping agent. Together, these contributions offer a human-centered foundation for designing and evaluating the next generation of AI agents, moving the field's focus from the correctness of generated code toward the dynamics of true collaborative intelligence.

In-IDE Generation-based Information Support with a Large Language Model

Developers often face challenges in code understanding, which is crucial for building and maintaining high-quality software systems. Code comments and documentation can provide some context for the code, but are often scarce or missing. This challenge has become even more pressing with the rise of large language model (LLM) based code generation tools. To understand unfamiliar code, most software developers rely on general-purpose search engines to search through various programming information resources, which often requires multiple iterations of query rewriting and information foraging. More recently, developers have turned to online chatbots powered by LLMs, such as ChatGPT, which can provide more customized responses but also incur more overhead as developers need to communicate a significant amount of context to the LLM via a textual interface. In this study, we provide the investigation of an LLM-based conversational UI in the IDE. We aim to understand the promises and obstacles for tools powered by LLMs that are contextually aware, in that they automatically leverage the developer's programming context to answer queries. To this end, we develop an IDE Plugin that allows users to query back-ends such as OpenAI's GPT-3.5 and GPT-4 with high-level requests, like: explaining a highlighted section of code, explaining key domain-specific terms, or providing usage examples for an API. We conduct an exploratory user study with 32 participants to understand the usefulness and effectiveness, as well as individual preferences in the usage of, this LLM-powered information support tool. The study confirms that this approach can aid code understanding more effectively than web search, but the degree of the benefit differed by participants' experience levels.