mlsanityData to 14 Oct 2026

Exploring the intersection of Generative AI and Software Development

arXiv 2312.14262 ↗First posted 21 Dec 2023cs.SE cs.AIcited by 2

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Abstract (arXiv)

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. Spanning from project management to support and updates, we meticulously map the demands of each development stage and unveil the potential of generative AI in addressing them. Techniques such as zero-shot prompting, self-consistency, and multimodal chain-of-thought are explored, showcasing their unique capabilities in enhancing generative AI models. The significance of vector embeddings, context, plugins, tools, and code assistants is underscored, emphasizing their role in capturing semantic information and amplifying generative AI capabilities. Looking ahead, this intersection promises to elevate productivity, improve code quality, and streamline the software development process. This whitepaper serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering, fostering innovation and collaboration for a qualitative leap in the efficiency and effectiveness of software development.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

11 of 35 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  5. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  7. 2023Large Language Modelscited by 780
  8. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  9. 2022The Stack: 3 TB of permissively licensed source codecited by 359
  10. 2023Active Prompting with Chain-of-Thought for Large Language Modelscited by 154
  11. 2023Guiding Large Language Models via Directional Stimulus Promptingcited by 95

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025On the Use of Agentic Coding Manifests: An Empirical Study of Claude Codecited by 14
  2. 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domainscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative AI and the Transformation of Software Development Practices
  2. 2026AI-driven Software Development: A Pragmatic Path to Agentic Development Processes
  3. 2026Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges
  4. 2026Human-AI Collaboration and the Transformation of Software Engineering Work
  5. 2026Towards Actionable Visualization: Ten Years Later, What Generative AI Changes and What It Cannot ↗
  6. 2026The Rise of AI-Native Software Engineering: Implications for Practice, Education, and the Future Workforce
  7. 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directions
  8. 2026Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings