# The State of Generative AI in Software Development: Insights from Literature and a Developer Survey

arXiv: https://arxiv.org/abs/2603.16975 · HTML: https://mlsanity.com/paper/2603.16975/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks](https://mlsanity.com/paper/2308.04451/) (2023)
- [Requirements are All You Need: From Requirements to Code with LLMs](https://mlsanity.com/paper/2406.10101/) (2024)
- [Language Models for Code Optimization: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2501.01277/) (2025)
- [Empowering Agile-Based Generative Software Development through Human-AI Teamwork](https://mlsanity.com/paper/2407.15568/) (2024)
- [Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes](https://mlsanity.com/paper/2311.18452/) (2023)
- [Exploring Human-AI Collaboration in Agile: Customised LLM Meeting Assistants](https://mlsanity.com/paper/2404.14871/) (2024)
- [The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks](https://mlsanity.com/paper/2506.10051/) (2025)
- [PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows](https://mlsanity.com/paper/2310.15435/) (2023)
- [Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow](https://mlsanity.com/paper/2405.01543/) (2024)
- [Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study](https://mlsanity.com/paper/2509.20353/) (2025)
- [Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development](https://mlsanity.com/paper/2409.17434/) (2024)
- [The Impact of Large Language Models (LLMs) on Code Review Process](https://mlsanity.com/paper/2508.11034/) (2025)
- [CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics](https://mlsanity.com/paper/2508.16713/) (2025)
- [A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development](https://mlsanity.com/paper/2505.07664/) (2025)

## Built on by (most cited first, 2 of 2)

- [Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI](https://mlsanity.com/paper/2607.01418/) (2026)
- [Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda](https://mlsanity.com/paper/2608.21107/) (2026)

## Abstract (arXiv)

Generative Artificial Intelligence (GenAI) rapidly transforms software engineering, yet existing research remains fragmented across individual tasks in the Software Development Lifecycle. This study integrates a systematic literature review with a survey of 65 software developers. The results show that GenAI exerts its highest impact in design, implementation, testing, and documentation, where over 70 % of developers report at least halving the time for boilerplate and documentation tasks. 79 % of survey respondents use GenAI daily, preferring browser-based Large Language Models over alternatives integrated directly in their development environment. Governance is maturing, with two-thirds of organizations maintaining formal or informal guidelines. In contrast, early SDLC phases such as planning and requirements analysis show markedly lower reported benefits. In a nutshell, GenAI shifts value creation from routine coding toward specification quality, architectural reasoning, and oversight, while risks such as uncritical adoption, skill erosion, and technical debt require robust governance and human-in-the-loop mechanisms.