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

TL;DR

Generative AI significantly enhances efficiency in software design, implementation, testing, and documentation, with widespread daily use among developers, but also introduces risks that require careful governance and oversight.

Contribution

This study combines a systematic literature review with a developer survey to provide comprehensive insights into GenAI's impact on software engineering practices.

Findings

GenAI reduces time for boilerplate and documentation tasks by over 50%.

79% of developers use GenAI daily, preferring browser-based LLMs.

Governance practices are increasingly formalized in organizations.

Abstract

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.