# Navigating the Complexity of Generative AI Adoption in Software Engineering

arXiv: https://arxiv.org/abs/2307.06081 · HTML: https://mlsanity.com/paper/2307.06081/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2023.

## Built on by (most cited first, 30 of 32)

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- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025)
- [Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains](https://mlsanity.com/paper/2506.14567/) (2025)
- [Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy](https://mlsanity.com/paper/2507.08594/) (2025)
- [Exploring the Impact of Generative Artificial Intelligence on Software Development in the IT Sector: Preliminary Findings on Productivity, Efficiency and Job Security](https://mlsanity.com/paper/2508.16811/) (2025)
- [Learning to Code with Context: A Study-Based Approach](https://mlsanity.com/paper/2512.05242/) (2025)
- [Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study](https://mlsanity.com/paper/2601.16700/) (2026)
- [Artificial Intelligence as a Catalyst for Innovation in Software Engineering](https://mlsanity.com/paper/2603.10994/) (2026)
- [LLM-based Automated Architecture View Generation: Where Are We Now?](https://mlsanity.com/paper/2603.21178/) (2026)
- [Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings](https://mlsanity.com/paper/2604.20436/) (2026)
- [Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment](https://mlsanity.com/paper/2606.11672/) (2026)
- [AI-driven Software Development: A Pragmatic Path to Agentic Development Processes](https://mlsanity.com/paper/2606.15283/) (2026)
- [On the Prospects of Dynamic LLM Conversations in Software Development](https://mlsanity.com/paper/2608.30756/) (2026)

## Abstract (arXiv)

In this paper, the adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed using a mixed-methods approach for an extensive comprehension of AI adoption. An initial structured interview was conducted with 100 software engineers, employing the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theories. A theoretical model named the Human-AI Collaboration and Adaptation Framework (HACAF) was deduced using the Gioia Methodology, characterizing AI adoption in software engineering. This model's validity was subsequently tested through Partial Least Squares - Structural Equation Modeling (PLS-SEM), using data collected from 183 software professionals. The results indicate that the adoption of AI tools in these early integration stages is primarily driven by their compatibility with existing development workflows. This finding counters the traditional theories of technology acceptance. Contrary to expectations, the influence of perceived usefulness, social aspects, and personal innovativeness on adoption appeared to be less significant. This paper yields significant insights for the design of future AI tools and supplies a structure for devising effective strategies for organizational implementation.