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

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.

Figures (5)

• Figure 5.1: Development methodology and age distribution
• Figure 5.2: GenAI tool usage
• Figure 5.3: Impact of GenAI by SDLC phase
• Figure 5.4: Estimated time saving with GenAI by coding task
• Figure 5.5: GenAI policies and guidelines in the respondent's companies