# Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap

arXiv: https://arxiv.org/abs/2410.06107 · HTML: https://mlsanity.com/paper/2410.06107/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 117 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [RouteLLM: Learning to Route LLMs with Preference Data](https://mlsanity.com/paper/2406.18665/) (2024)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [RouterBench: A Benchmark for Multi-LLM Routing System](https://mlsanity.com/paper/2403.12031/) (2024)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [LAB: Large-Scale Alignment for ChatBots](https://mlsanity.com/paper/2403.01081/) (2024)
- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [TRACED: Execution-aware Pre-training for Source Code](https://mlsanity.com/paper/2306.07487/) (2023)
- [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022)
- [Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware](https://mlsanity.com/paper/2402.15943/) (2024)
- [Towards Semantic Versioning of Open Pre-trained Language Model Releases on Hugging Face](https://mlsanity.com/paper/2409.10472/) (2024)

## Built on by (most cited first, 30 of 30)

- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [AIDev: Studying AI Coding Agents on GitHub](https://mlsanity.com/paper/2602.09185/) (2026)
- [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025)
- [Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents](https://mlsanity.com/paper/2411.03455/) (2024)
- [How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](https://mlsanity.com/paper/2601.17581/) (2026)
- [Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models](https://mlsanity.com/paper/2410.09012/) (2024)
- [Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time](https://mlsanity.com/paper/2604.00917/) (2026)
- [AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub](https://mlsanity.com/paper/2604.03551/) (2026)
- [Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study](https://mlsanity.com/paper/2605.22534/) (2026)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agents](https://mlsanity.com/paper/2602.04226/) (2026)
- [Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study](https://mlsanity.com/paper/2602.00164/) (2026)
- [How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses](https://mlsanity.com/paper/2602.17084/) (2026)
- [From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap](https://mlsanity.com/paper/2410.20791/) (2024)
- [Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models](https://mlsanity.com/paper/2411.09837/) (2024)
- [Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement](https://mlsanity.com/paper/2505.20973/) (2025)
- [Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRs](https://mlsanity.com/paper/2603.27524/) (2026)
- [The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)](https://mlsanity.com/paper/2505.10640/) (2025)
- [How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study](https://mlsanity.com/paper/2512.24636/) (2025)
- [Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review](https://mlsanity.com/paper/2604.13103/) (2026)
- [IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software Development](https://mlsanity.com/paper/2604.16399/) (2026)
- [Quality and Security Signals in AI-Generated Python Refactoring Pull Requests](https://mlsanity.com/paper/2605.21453/) (2026)
- [AI-driven Software Development: A Pragmatic Path to Agentic Development Processes](https://mlsanity.com/paper/2606.15283/) (2026)
- [Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents](https://mlsanity.com/paper/2607.12428/) (2026)
- [Early Adoption of Agentic Coding Tools by GitHub Projects](https://mlsanity.com/paper/2607.14037/) (2026)
- [Understanding the Architecture of Coding Agents: An Exploratory Study Using a Research Prototype](https://mlsanity.com/paper/2608.10934/) (2026)
- [Spec-Driven Development for Agentic Software Engineering: Harnessing Human-Agent Teamwork](https://mlsanity.com/paper/2609.00252/) (2026)
- [It Is Not My Code Anymore](https://mlsanity.com/paper/2609.09022/) (2026)
- [ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Coding](https://mlsanity.com/paper/2609.33113/) (2026)

## Abstract (arXiv)

The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-centric, conversation-oriented development between human developers and AI teammates. SE 3.0 envisions AI systems evolving beyond task-driven copilots into intelligent collaborators, capable of deeply understanding and reasoning about software engineering principles and intents. We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership, IDE.next for intent-centric conversation-oriented development, Compiler.next for multi-objective code synthesis, and Runtime.next for SLA-aware execution with edge-computing support. Our vision addresses the inefficiencies and cognitive strain of SE 2.0 by fostering a symbiotic relationship between human developers and AI, maximizing their complementary strengths. We also present a roadmap of challenges that must be overcome to realize our vision of SE 3.0. This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.