Integrating GenAI into Software Work: Product Quality, Collaboration, and Process Impacts under an ISO/IEC 25010 Lens

Context: Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has rapidly emerged as a transformative force in software engineering, supporting activities such as code generation, refactoring, testing, and documentation. Despite their growing adoption, empirical evidence remains limited regarding how LLM-based tools influence established dimensions of software product quality such as those defined in ISO/IEC 25010 as well as collaboration and innovation within software teams. Goal: This study investigates how the integration of GenAI tools affects software quality, human-centered factors, and development processes across the software lifecycle. We aim to provide a holistic understanding of both the opportunities and risks associated with their adoption in professional environments. Method: We conducted a survey with 121 software practitioners from 26 Brazilian states, spanning public and private sectors. The questionnaire comprised 38 closed- and open-ended questions. Quantitative data were analyzed using descriptive statistics analysis, while qualitative answers underwent open and axial coding following Grounded Theory principles. Results: Practitioners reported positive perceptions of GenAI in functional suitability, performance efficiency, maintainability, and flexibility, as well as in usability and adaptability. They emphasized productivity gains, faster bug detection, and automation of repetitive tasks, but expressed concerns about reliability, security, and safety. Open-ended responses revealed improvements in collaboration, communication, and innovation, particularly through rapid prototyping and creative ideation, alongside risks such as overreliance, diminished peer interaction, and a lack of long-term evidence on software quality. Conclusions: GenAI tools are perceived as important complements to software development, enhancing productivity, code quality, and team collaboration when used under human-in-the-loop oversight. Their integration demands governance mechanisms that ensure reliability, accountability, and sustainable quality improvements.