cs.SEMar 17, 2026

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

Authors: Vincent Gurgul, Robin Gubela, Stefan Lessmann

Organizations: Chair of Information Systems Humboldt-Universität zu Berlin Unter den Linden 6, 10117 Berlin · Chair of Information Systems Hochschule für Technik und Wirtschaft Berlin Treskowallee 8, 10318 Berlin · Bucharest University of Economic Studies 6 Piata Romana, 010374, Romania

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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 & tables

Figure 5.1: Development methodology and age distribution
Figure 5.2: GenAI tool usage
Figure 5.3: Impact of Generative Artificial Intelligence by Software Development Life Cycle phase
Figure 5.4: Estimated time saving with Generative Artificial Intelligence by coding task
Figure 5.5: GenAI policies and guidelines in the respondent’s companies

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