arXiv:2511.02399v3 Announce Type: replace-cross Abstract: Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipelines, which oversimplify the iterative nature of real-world development and struggle with complex, large-scale projects. To address these limitations, we propose EvoDev, an iterative software development framework inspired by feature-driven development. EvoDev decomposes user requirements into a set of user-valued feature
Advances in large language models are enabling more sophisticated agentic systems, making the automation of complex workflows like software development feasible.
This development indicates a maturation in AI agent capabilities, moving from task-specific automation to more complex, iterative engineering processes that can significantly impact productivity and the software development lifecycle.
The previous linear approaches to AI-driven software development will be superseded by iterative, feature-driven frameworks, better reflecting real-world development practices and enabling AI to tackle larger projects.
- · AI agent developers
- · Software companies adopting AI-driven development
- · Knowledge workers (leveraging AI tools)
- · Traditional software development consultancies (resistant to AI)
- · Entry-level software testers
AI models will increasingly develop and maintain software with human oversight.
Software development cycles will accelerate, leading to faster innovation in other tech sectors.
The definition of 'software developer' will evolve, shifting focus from coding to AI model management and architectural design.
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Read at arXiv cs.AI