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This study combines a systematic literature review with a survey of 65 software developers to assess the impact of Generative AI (GenAI) across the Software Development Lifecycle (SDLC). The findings reveal that GenAI significantly reduces time spent on design, implementation, testing, and documentation, with over 70% of developers reporting at least a 50% time reduction for boilerplate code and documentation. The survey also highlights a preference for browser-based Large Language Models and the increasing adoption of GenAI governance guidelines within organizations.
GenAI is already halving the time developers spend on boilerplate and documentation, but its real potential lies in shifting focus from routine coding to higher-level tasks like specification quality and architectural reasoning.
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.
