A three-level taxonomy inspired by autonomous driving that distinguishes degrees of autonomy along a roadmap from today’s AI-assisted development workflows to fully autonomous software development in which AI systems autonomously identify demands and design, implement, verify, and maintain software without human oversight is introduced.
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This ar...
This paper presents a layered architecture for AI-integrated development systems, encompassing prompt formulation, semantic interpretation, code generation, and validation mechanisms, and explores the dynamics of human–AI interaction, highlighting the importance of trust calibration, control boundaries, and iterative r...
Yasin Arik· International Journal of Res...· 0 citations
Developing a GenAI agent that autonomously formulates, prioritizes, and completes a limited set of code improvement tasks enterprise-wide is reported, and a Difference-in-Differences analysis found that automated improvements had an effect on the reduction of future maintenance effort that was as strong as or stronger...
This review traces how quality engineering has changed, from rule-based automation to self-adjusting test frameworks, and looks at the technology behind Autonomous Quality Agents: Large Language Models that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that...
Vanshita Agarwal· International journal for ad...· 0 citations
This study characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle, offering a more nuanced understanding of their benefits and limitations in real-world practices.
Iren Mazloomzadeh, Mohammad Mehdi Morovati, F. Khomh· arXiv.org· 0 citations
This survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.
Hao Zhou, Hai-Chuan Hu, Tianyu Luo et al.· 0 citations
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