Jul 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 53 references
Computer Science
TL;DR
This paper builds on earlier findings to propose a forward-looking vision of GenAI as an instant, readily available co-developer in hybrid software design and revisit documented challenges from an earlier study on hybrid collaboration.
Abstract
The increasing adoption of hybrid and remote work has reshaped how software teams communicate, collaborate, and make design decisions. Our prior study showed that hybrid setups often result in missed discussions, fragmented understanding, and limited documentation of design rationale. At the same time, Generative AI (GenAI) tools–such as GitHub Copilot and ChatGPT–are becoming embedded in development workflows, providing support in code generation. While GenAI’s role in software development is gaining attention, its impact on early-stage design ideation activities and collaboration in hybrid teams remains underexplored. This paper builds on earlier findings to propose a forward-looking vision of GenAI as an instant, readily available co-developer in hybrid software design. We revisit documented challenges from our earlier study on hybrid collaboration and outline how GenAI could address these issues by facilitating asynchronous participation, surfacing undocumented rationale, and preserving design continuity. We present a conceptual framing of GenAI-supported collaboration and propose a research agenda to guide future studies on integrating GenAI into hybrid settings.
Design systems are widely used to ensure consistency and scalability in modern UI development, yet translating design artifacts into system-aligned code remains challenging. Developers must interpret visual designs and map them to appropriate components and implementation details, often relying on external documentation. While AI-assisted coding tools can accelerate development, they often lack structured design-system context, limiting their effectiveness in design-to-code workflows. In this paper, we investigate how a structured design context can influence developer experience. We present an empirical within-subject study with N=30 frontend developers comparing a traditional documentation-driven workflow to an MCP-supported workflow integrating Figma Dev Mode, Code Connect, and AI-assisted coding tools. The results show significant improvements in implementation accuracy, task efficiency, and perceived usability when a structured design context is available. Our findings suggest that AI-supported workflows can improve developer experience by reducing ambiguity in design interpretation while maintaining the need for human validation and documentation support.
Surbhi Rajpal, Andreas Riener· Proceedings of Mensch und Co...· 0 citations
Interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023 offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
Benyamin T. Tabarsi, Heidi Reichert, Sam Gilson et al.· Empirical Software Engineeri...· 21 citations· ⚡1
AI agents are becoming a fundamental part of modern software creation, helping developers in generating code, debugging, designing systems, etc. But there is a clear difference between how beginners and experienced software engineers get benefits from these tools. Newbies usually depend on agents for one-time prompts and quick answers, whereas mature users utilize them through well-defined, repeated workflows that raise productivity and consistency. In this article, we discuss this difference and emphasize that getting the full potential does not merely depend on better prompts but on workflows driven by instructions developers create clear and reusable instruction files to direct agent behavior across tasks. When developers stop seeing agents only as chat interfaces but as programmable collaborators, they can produce more reliable and high-quality outputs. We offer in our paper methods like designing modular instructions, narrowing down the context, and iterative refinement loops, as well as a case study illustrating how a team made a code review more efficient and minimized the rework by making agent instructions standard. The results stress that structured forms of interaction rather than sporadic use are the main ways to tap into advanced features. Our paper provides a conceptual model for agent usage at large scale, hands-on advice for the implementation of instruction files in actual settings, and validation that skillful developers can far exceed basic usage by adopting orderly, system-like approaches to agent collaboration.
Madhurima Kommuru, Srujana Pulipaka· International Journal of Mod...· 0 citations
An integrated human‑automation teaming framework is presented that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers and provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Jelle A Van Dijk, Rosa van Tuijn, Renske Verwaal-Bootsma et al.· AHFE International· 0 citations
Context: Conversational Large Language Models (CLLMs) can automatically generate code by collaborating with users through natural language. However, poor collaboration can lead to poor quality output. Objective: This exploratory study aims to investigate how humans and CLLMs can collaborate as peers through vibe coding, an approach that integrates principles from prompt engineering, agile design, and human-AI co-creation to enhance collaboration. Method: We designed four interaction models representing different collaboration patterns in the software development process: the solo model (human-only development), the collaborative model (human-CLLM collaboration), the fully automated model (development autonomously performed by a CLLM), and the agentic model (development autonomously performed by the MetaGPT~X platform). Based on these models, we implemented corresponding Test-Driven Development (TDD) workflows using structured prompts and Python scripts. We then conducted a controlled pre-experimental study with TDD professionals to compare the solo and collaborative workflows. In addition, we performed repeated exploratory executions of fully automated and agentic workflows on the same development tasks to obtain complementary evidence. Results: Our findings suggest that the choice of interaction model should depend on the development objective. Agentic workflows are best suited for rapid development and functionally correct production code but may introduce additional implementation complexity. However, they may also introduce additional implementation decisions that are not explicitly required by the functional specifications, resulting in untested decision points. In contrast, collaborative workflows produce higher-quality, better-organized test suites. Conclusions: Our work explored how...
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