# AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation

arXiv: https://arxiv.org/abs/2510.00591 · HTML: https://mlsanity.com/paper/2510.00591/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Natural Language to Code Translation with Execution](https://mlsanity.com/paper/2204.11454/) (2022)
- [Large Language Model-Aware In-Context Learning for Code Generation](https://mlsanity.com/paper/2310.09748/) (2023)
- [Unified Software Engineering Agent as AI Software Engineer](https://mlsanity.com/paper/2506.14683/) (2025)
- [Agile Software Development Methods: Review and Analysis](https://mlsanity.com/paper/1709.08439/) (2017)
- [Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema Adherence](https://mlsanity.com/paper/2502.14905/) (2025)
- [Exploring the Potential of Diffusion Large Language Models in Code Generation](https://mlsanity.com/paper/2509.11252/) (2025)
- [aiXcoder-7B-v2: Training LLMs to Fully Utilize the Long Context in Repository-level Code Completion](https://mlsanity.com/paper/2503.15301/) (2025)
- [Correctness-Guaranteed Code Generation via Constrained Decoding](https://mlsanity.com/paper/2508.15866/) (2025)
- [DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern](https://mlsanity.com/paper/2509.24975/) (2025)

## Built on by (most cited first, 3 of 3)

- [CoACT: Action-Preserving Observation Compression for Coding Agents](https://mlsanity.com/paper/2607.02911/) (2026)
- [Learning from Execution: Self-Evolving Memory for Private-Library Code Generation](https://mlsanity.com/paper/2604.24222/) (2026)
- [Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code](https://mlsanity.com/paper/2606.11817/) (2026)

## Abstract (arXiv)

Software automation has long been a central goal of software engineering, striving for software development that proceeds without human intervention. Recent efforts have leveraged Artificial Intelligence (AI) to advance software automation with notable progress. However, current AI functions primarily as assistants to human developers, leaving software development still dependent on explicit human intervention. This raises a fundamental question: Can AI move beyond its role as an assistant to become a core component of software, thereby enabling genuine software automation? To investigate this vision, we introduce AI-Driven Self-Evolving Software, a new form of software that evolves continuously through direct interaction with users. We demonstrate the feasibility of this idea with a lightweight prototype built on a multi-agent architecture that autonomously interprets user requirements, generates and validates code, and integrates new functionalities. Case studies across multiple representative scenarios show that the prototype can reliably construct and reuse functionality, providing early evidence that such software systems can scale to more sophisticated applications and pave the way toward truly automated software development. We make code and cases in this work publicly available at https://anonymous.4open.science/r/live-software.