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Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

David Lo

TL;DR

This paper surveys the history and current landscape of AI4SE, identifies trust and synergy as the central obstacles to widespread adoption, and envisions Software Engineering 2.0 where trustworthy, synergistic AI4SE agents act as first-class, autonomous collaborators within dynamic human-AI teams. It proposes two concrete roadmaps—one for achieving trustworthy AI4SE and another for synergistic AI4SE—detailing strategies across intrinsic/extrinsic trust, workflow-aware design, and multi-agent interactions. By outlining 15 open challenges and emphasizing governance, explainability, privacy, licensing, and attack-resilience, the work articulates a comprehensive path toward a symbiotic human-AI software engineering future. The paper aims to catalyze cross-disciplinary research and industry collaboration to actualize a responsible, efficient, and scalable SE 2.0 ecosystem.

Abstract

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.

Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

TL;DR

This paper surveys the history and current landscape of AI4SE, identifies trust and synergy as the central obstacles to widespread adoption, and envisions Software Engineering 2.0 where trustworthy, synergistic AI4SE agents act as first-class, autonomous collaborators within dynamic human-AI teams. It proposes two concrete roadmaps—one for achieving trustworthy AI4SE and another for synergistic AI4SE—detailing strategies across intrinsic/extrinsic trust, workflow-aware design, and multi-agent interactions. By outlining 15 open challenges and emphasizing governance, explainability, privacy, licensing, and attack-resilience, the work articulates a comprehensive path toward a symbiotic human-AI software engineering future. The paper aims to catalyze cross-disciplinary research and industry collaboration to actualize a responsible, efficient, and scalable SE 2.0 ecosystem.

Abstract

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.
Paper Structure (12 sections, 4 figures)

This paper contains 12 sections, 4 figures.

Table of Contents

  1. Introduction and Brief History of AI4SE
  2. Key Challenges of AI4SE: Trust and Synergy
  3. Need for Trust
  4. Need for Synergy
  5. Vision: Software Engineering 2.0
  6. Roadmap to Trustworthy AI4SE
  7. Enhance Intrinsic Trustworthiness of AI4SE Solutions
  8. Enhance Extrinsic Trustworthiness of AI4SE Solutions
  9. Roadmap to Synergistic AI4SE
  10. Understand synergy
  11. Build synergistic AI4SE capabilities
  12. Summary and Concluding Remarks

Figures (4)

  • Figure 1: Overview of the Paper with Its Six Sections
  • Figure 2: Software Engineering 2.0 (SE 2.0) will see AI4SE solutions transitioning from smart tools to smart workmates. These intelligent agents will exercise responsible autonomy. They will be first-class citizens, taking different roles as assistants, peers, and even managers in software engineering projects. They will be well integrated in dynamic software engineering teams. There will be human-human (H-H), human-agent (H-A), and agent-agent (A-A) interactions. There will also be a new economy surrounding the AI4SE agent market as well as solid foundations in law and ethics supporting SE 2.0.
  • Figure 3: Roadmap to Trustworthy AI4SE
  • Figure 4: Roadmap to Synergistic AI4SE