# The Transformative Influence of LLMs on Software Development & Developer Productivity

arXiv: https://arxiv.org/abs/2311.16429 · HTML: https://mlsanity.com/paper/2311.16429/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impaired](https://mlsanity.com/paper/2503.16491/) (2025)
- [Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse](https://mlsanity.com/paper/2403.09167/) (2024)
- [Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study](https://mlsanity.com/paper/2602.14357/) (2026)
- [Enhancing LLM Code Generation Capabilities through Test-Driven Development and Code Interpreter](https://mlsanity.com/paper/2511.12823/) (2025)
- [Formally Verified Code Synthesis for Structured Data Translation in a Medical Internet of Things](https://mlsanity.com/paper/2606.20776/) (2026)

## Abstract (arXiv)

The increasing adoption and commercialization of generalized Large Language Models (LLMs) have profoundly impacted various aspects of our daily lives. Initially embraced by the computer science community, the versatility of LLMs has found its way into diverse domains. In particular, the software engineering realm has witnessed the most transformative changes. With LLMs increasingly serving as AI Pair Programming Assistants spurred the development of specialized models aimed at aiding software engineers. Although this new paradigm offers numerous advantages, it also presents critical challenges and open problems. To identify the potential and prevailing obstacles, we systematically reviewed contemporary scholarly publications, emphasizing the perspectives of software developers and usability concerns. Preliminary findings underscore pressing concerns about data privacy, bias, and misinformation. Additionally, we identified several usability challenges, including prompt engineering, increased cognitive demands, and mistrust. Finally, we introduce 12 open problems that we have identified through our survey, covering these various domains.