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

2026-03-17

AI Summary

This study addresses the lack of a systematic understanding of generative artificial intelligence’s role across the full software development lifecycle. Through a systematic literature review complemented by structured surveys of 65 developers, this work integrates empirical data with existing research to comprehensively evaluate the real-world impact and adoption patterns of large language models (LLMs) in each development phase. Findings indicate that over 70% of developers save more than 50% of their time on boilerplate code generation and documentation tasks, and 79% use browser-based LLMs daily. While nascent governance mechanisms are emerging, benefits in early-stage activities—such as requirements elicitation and architectural design—remain limited. The results suggest that generative AI is shifting the locus of development value from coding toward upstream design activities.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Generative AI
• Software Development Lifecycle
• Developer Survey
• AI Governance
• Technical Debt

Innovation

• Generative AI
• Software Development Lifecycle
• Developer Survey
• LLM Adoption
• AI Governance