Impact of Generative Artificial Intelligence in Software Development Life Cycle : A Mixed-Methods Study in Finland and Abroad Using Concurrent Triangulation Design Approach

Abstract

Generative Artificial Intelligence (AI) is revolutionising the Software Development Life Cycle (SDLC) by enhancing productivity, efficiency, and collaboration, especially in coding, testing, and documentation. This study examines the adoption and impact of generative AI tools in SDLC phases within Finland and internationally, aiming to address the growing integration of AI in software development. The research was conducted to assess how AI influences SDLC phases, exploring both its advantages and challenges. A mixed-methods approach with a concurrent triangulation design to validate and cross-verify findings was employed. It combined surveys and secondary data to provide a comprehensive understanding. The quantitative survey analysed adoption rates and user perceptions, while secondary data validated the findings with broader industry perspectives. Findings reveal that AI significantly improves coding and testing efficiency, but adoption remains limited in earlier phases like requirement gathering and design due to complexity and subjectivity. Developers widely adopt AI for routine tasks, reporting increased productivity, though they express concerns about job displacement, data security, and ethical risks. These results highlight the importance of improving AI's reliability and contextual understanding to maximise its potential. Addressing these challenges could pave the way for AI's broader adoption in software development. Future research should focus on advancing AI capabilities in early SDLC phases, such as requirement gathering, to enhance its value in complex tasks.