The Impact of Code-Generating AI on the Work of Programmers

• Autores: Rivas Verastegui, Kevin, Tirado Ruiz, Elmo, Torres Villanueva, Marcelino
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/228
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/228; https://doi.org/10.48168/innosoft.s23.a228; https://purl.org/42411/s23/a228; https://n2t.net/ark:/42411/s23/a228
• Nivel de acceso: acceso abierto
• Materia: automation; software development; GitHub Copilot; artificial intelligence; productivity; automatización; desarrollo de software; inteligencia artificial; productividad

This study analyzed the impact of code-generating artificial intelligences (AI), such as GitHub Copilot, on programmers' work. It aimed to determine how these tools affect productivity and code quality, differentiating their effects based on developers' experience levels. A systematic literature review and tool analysis were conducted, using the PRISMA methodology to assess experimental studies and usage reports. Results revealed that code-generating AIs improved productivity by up to 55.8% for experienced programmers, while less experienced developers exhibited increased reliance and confidence in generated code, leading to security risks. Additionally, benefits included reduced development times and democratized access to software, though ethical and technical risks related to overdependence and loss of fundamental skills were noted. These findings underscore the need for strategies that combine these technologies with continuous learning and responsible practices. In conclusion, code-generating AIs are catalysts for software development but require a balanced approach to maximize their advantages and address their challenges