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Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers

arXiv 2404.10225 ↗First posted 16 Apr 2024cs.SE cs.AIcited by 11

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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.

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6 of 49 references.

References · 6

  1. 2021Program Synthesis with Large Language Modelscited by 3,509
  2. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  5. 2024Bugs in Large Language Models Generated Code: An Empirical Studycited by 59
  6. 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 2.

11 papers.