Desarrollo de un sitio web para la generación automatizada de casos de pruebas unitarias utilizando la API de Open AI.

Abstract

Developers and testers face significant challenges when performing unit tests manually. This includes the considerable time investment required to write and execute tests, which can complicate project delivery dates affecting coverage. Lack of experience in creating numerous unit tests is another obstacle; the need for manual adjustments to tests due to changing requirements also contributes to complexity and excessive time consumption in this process; therefore, the purpose of this integration curricular work was to develop a web site for the automated generation of unit test cases using the Open AI API. The type of study used for development was applied, and quantitative research was conducted using inductive, analytical, and observation methods along with the Scrumban methodology. Applying the Scrumban methodology allowed managing the development of the project following an agile approach using technologies such as JavaScript with its React framework, Node.js with Express, and a Firestore database, following a component-based and client-server architecture; these developed solutions were deployed to Netlify for the frontend and Render for the backend. Finally, a process was carried out to evaluate the solution designed for students in the eighth semester of the Escuela Superior Politécnica de Chimborazo from October 2023 to March 2024. To assess the quality of the software, the ISO/IEC 25010 standard was used, focusing on measuring temporal behavior. The results show that unit testing development times using the developed solution are significantly reduced by 783.13% compared to manual tests.