mlsanityData to 7 Oct 2026

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

arXiv 2309.04142 ↗First posted 8 Sep 2023cs.SE cs.AIcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 48 of its 155 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

48 of 155 references.

References · 48

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36 more
  1. 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 6; 2026: 3.

19 papers.

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  1. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  2. 2024Calibration and Correctness of Language Models for Codecited by 49
  3. 2024Human-In-the-Loop Software Development Agentscited by 42
  4. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 10
  5. 2023Pitfalls in Language Models for Code Intelligence: A Taxonomy and Surveycited by 9
  6. 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AIcited by 6
  7. 2025On LLMs' Internal Representation of Code Correctnesscited by 5
  8. 2023LUNA: A Model-Based Universal Analysis Framework for Large Language Modelscited by 4
  9. 2025Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassiancited by 4
  10. 2024GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Trainingcited by 3
  11. 2024Morescient GAI for Software Engineering (Extended Version)cited by 1
  12. 2025Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findingscited by 1
7 more
  1. 2025Open the Oyster: Empirical Evaluation and Improvement of Code Reasoning Confidence in LLMscited by 1
  2. 2026PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testingcited by 1
  3. 2026Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainabilitycited by 1
  4. 2024Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localizationcited by 0
  5. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  6. 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshopcited by 0
  7. 2026From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systemscited by 0