The use of the AI-based programming assistant, GitHub Copilot, in Software Quality: A systematic literature review (Research, Society and Development, 2026)
The purpose of this paper is to investigate how the scientific literature has reported the use of GitHub Copilot and its impact on code quality. The research was conducted through a systematic literature review, following steps such as defining inclusion and exclusion criteria, searching in databases available through the CAPES Portal of Journals, selecting relevant studies, and performing a qualitative analysis of the selected works. Fifteen studies were identified and analyzed, addressing different aspects of GitHub Copilot, including security issues, the influence of prompt structure, impacts on productivity, performance comparisons with human developers, and the challenges faced by users. The findings highlight both the benefits and limitations of using the tool. The tool proves to be a useful support for developers, serving as a good starting point and guide for coding tasks. However, due to its tendency to generate insecure code and its limited capacity to handle complex tasks, it is not recommended for use in isolation. Moreover, outcomes also depend on how developers interact with GitHub Copilot, as excessive reliance and poorly structured prompts may lead to rework — especially among novice programmers, who represent the tool’s primary user base.