Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
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Abstract (arXiv)
The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of these copilots falls short in addressing the broader goals and complexities inherent in software engineering (SE). In this paper, we propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers in a more holistic and context-aware manner. We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning. These AI partners engage in iterative, conversation-driven development processes, aligning closely with human goals and facilitating informed decision-making. We discuss the desired attributes of such AI pair programmers and outline key challenges that must be addressed to realize this vision. Ultimately, our work represents a shift from AI-augmented SE to AI-transformed SE by replacing code completion with a collaborative partnership between humans and AI that enhances both productivity and software quality.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 6 of its 49 references that are in the map. First posted on arXiv 16 Apr 2024.
Builds on
6 of 49 references.
References · 6
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
- 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
- 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
- 2024Bugs in Large Language Models Generated Code: An Empirical Studycited by 59
- 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
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Citing papers per year: 2024: 5; 2025: 4; 2026: 2.
11 papers.
- 2024HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agentcited by 24
- 2024Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Productscited by 11
- 2025Can Code Language Models Learn Clarification-Seeking Behaviors?cited by 5
- 2024A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Modelcited by 4
- 2026Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecyclecited by 3
- 2024LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliancecited by 2
- 2025SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systemscited by 2
- 2025AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Softwarecited by 2
- 2026Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requestscited by 1
- 2024CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purificationcited by 0
- 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmapcited by 0
- 2026Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle5 Jun 2026
- 2026Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests26 Jan 2026
- 2025AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software21 Sep 2025
- 2025SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems11 Jul 2025
- 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap26 May 2025
- 2025Can Code Language Models Learn Clarification-Seeking Behaviors?23 Apr 2025
- 2024LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance30 Dec 2024
- 2024CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification26 Oct 2024
- 2024Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products15 Oct 2024
- 2024A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model6 Aug 2024
- 2024HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent31 May 2024