Automatización de pruebas de software basadas en propiedades mediante ingeniería de prompts e inteligencia artificial

The increasing complexity of modern software development demands more efficient, comprehensive, and adaptive testing methodologies that ensure the reliability, robustness, and quality of applications, while also optimizing the costs and time associated with system maintenance and evolution. Although traditional testing approaches remain widely used, they present limitations in terms of coverage, scalability, and adaptability, especially when dealing with dynamic and constantly evolving systems. In this context, the integration of large language models (LLMs) through prompt engineering techniques emerges as a promising and innovative alternative for automating, enhancing, and expanding software testing processes. This work presents a tool that combines the generative capabilities of LLMs, accessible through specialized APIs, with the rigor of property-based testing (PBT). This synergy enables the automatic generation of test properties and the intelligent validation of code, facilitating early error detection and contributing to the development of more robust and reliable software from the early stages of the development lifecycle. Through prompt engineering, the tool guides the precise formulation of test properties and orchestrates the generation of diverse and relevant data. This approach aims to overcome the limitations of traditional methodologies by improving test coverage, reducing manual effort, and increasing scalability. The result is a more optimized verification process that promotes higher standards of software quality and reliability. This proposal represents a step forward in the intelligent automation of testing, integrating artificial intelligence with formal validation methodologies and opening new possibilities for its application in software engineering.