Assessing the Use of a Code Generation Assistant in Professional Software Development: An Experience Report - Osorio, Luiz Fernando Mendes; Santos Neto, Pedro de A. dos; Avelino, Guilherme; Lira, Werney Ayala Luz (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de Software, 2025)
AI-assisted code generation tools, such as GitHub Copilot, have gained attention from the software industry due to their potential to support development tasks. This paper presents an experience report from a case study on the adoption of GitHub Copilot by a team of developers at the Superintendence of Information Technology of the Federal University of Piauí (STI/UFPI), aiming to evaluate the effects of using Copilot in a real-world environment involving legacy system maintenance. The study was carried out over three months, comparing periods with and without the use of the tool. Key metrics such as code change rate and developers’ perceptions were analyzed. The results indicate a increase in the code change rate for most developers, particularly in bug-fixing tasks, while customization tasks showed a more nuanced outcome. Participants also reported positive feedback regarding Copilot’s usefulness. This paper discusses the challenges faced during the study’s execution, methodological limitations, and proposes directions for future research on the impact of AI assistants in diverse software development contexts.