mlsanityData to 7 Oct 2026

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

arXiv 2410.06107 ↗First posted 8 Oct 2024cs.SE cs.AIcited by 30

Not on the map. Why?

Cited by 22 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 26 of its 117 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

26 of 117 references.

References · 26

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  5. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  6. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  7. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  8. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
  9. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
  10. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  11. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  12. 2024DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligencecited by 325
14 more
  1. 2023How is ChatGPT's behavior changing over time?cited by 265
  2. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  3. 2024RouterBench: A Benchmark for Multi-LLM Routing Systemcited by 222
  4. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  5. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 150
  6. 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
  7. 2023Machine Culturecited by 55
  8. 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 44
  9. 2024LAB: Large-Scale Alignment for ChatBotscited by 39
  10. 2020A Survey on Deep Learning for Software Engineeringcited by 37
  11. 2023TRACED: Execution-aware Pre-training for Source Codecited by 27
  12. 2022Mutual Theory of Mind for Human-AI Communicationcited by 15
  13. 2024Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMwarecited by 6
  14. 2024Towards Semantic Versioning of Open Pre-trained Language Model Releases on Hugging Facecited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 19.

30 papers.

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  1. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHubcited by 53
  2. 2025Agentic Software Engineering: Foundational Pillars and a Research Roadmapcited by 40
  3. 2026AIDev: Studying AI Coding Agents on GitHubcited by 15
  4. 2024Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agentscited by 8
  5. 2026How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requestscited by 7
  6. 2024Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Modelscited by 6
  7. 2025The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineeringcited by 4
  8. 2025The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Projectcited by 4
  9. 2026AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHubcited by 4
  10. 2026Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Studycited by 3
  11. 2026Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agentscited by 3
  12. 2026How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responsescited by 3
18 more
  1. 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Timecited by 3
  2. 2026Why Are Agentic Pull Requests Merged or Rejected? An Empirical Studycited by 3
  3. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
  4. 2024Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Modelscited by 1
  5. 2025Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinementcited by 1
  6. 2025The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)cited by 0
  7. 2025How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Studycited by 0
  8. 2026Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRscited by 0
  9. 2026Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Reviewcited by 0
  10. 2026IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software Developmentcited by 0
  11. 2026Quality and Security Signals in AI-Generated Python Refactoring Pull Requestscited by 0
  12. 2026AI-driven Software Development: A Pragmatic Path to Agentic Development Processescited by 0
  13. 2026Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agentscited by 0
  14. 2026Early Adoption of Agentic Coding Tools by GitHub Projectscited by 0
  15. 2026Understanding the Architecture of Coding Agents: An Exploratory Study Using a Research Prototypecited by 0
  16. 2026Spec-Driven Development for Agentic Software Engineering: Harnessing Human-Agent Teamworkcited by 0
  17. 2026It Is Not My Code Anymorecited by 0
  18. 2026ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Codingcited by 0