Optimization of software engineering processes through the application of artificial intelligence

Manuel Cerecedo García and Carmen Carolina Ortega Hernández
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Manuel Cerecedo García: Instituto Tecnológico de Tapachula. Tapachula, México.
Carmen Carolina Ortega Hernández: Universidad Autónoma de Chiapas. Tapachula, México.

CognitivaTech: Ingeniería de Software Inteligente y Sistemas Adaptativos, 2026, vol. 3, issue 1, 51

Abstract: Artificial intelligence has become a strategic tool for improving processes related to software engineering, especially in activities associated with automation, data analysis, error detection, and the productivity of technological teams. This study aimed to analyze the relationship between the application of artificial intelligence, the optimization of software engineering processes, and software quality/productivity among workers from companies in Ecuador, Colombia, and Bolivia. The research was conducted under a quantitative approach, with a non-experimental design, descriptive-correlational scope, and cross-sectional nature. The sample consisted of 135 workers, proportionally distributed among the three countries. Data were collected through a structured survey using a five-point Likert scale, organized into three main variables. The results showed high mean scores for artificial intelligence application, process optimization, and software quality/productivity. In addition, positive and significant correlations were identified among the variables analyzed, with the strongest relationship found between process optimization and quality/productivity. It is concluded that artificial intelligence contributes to improving operational efficiency, reducing repetitive tasks, and strengthening software development quality, provided that its implementation is supported by training, human supervision, and appropriate technical criteria.

Keywords: artificial intelligence; software engineering; process optimization; productivity; software quality. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cxn:cognit:v:3:y:2026:i:1:id:51

DOI: 10.63688/cognitivatech202651

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