El futuro del testing de software: datos sintéticos & gemelos digitales

The integration of synthetic data and digital twins is transforming software engineering, enabling realistic simulations that accelerate innovation and optimize decision-making. These technologies overcome traditional limitations in accessing real-world data, facilitating the prediction of behaviors and the improvement of processes in complex sectors such as healthcare and logistics. This evolution redefines testing environments, offering unprecedented scalability and mitigating operational risks. However, this advancement presents significant challenges related to quality and reliability. It is crucial to ensure that synthetic data are representative and free from bias to prevent the propagation of errors in algorithms. Additionally, precise alignment is required between the generated data and the logic of digital twins, maintaining continuous updates with real-time information. Coordination among development, quality assurance, and data science teams becomes imperative to ensure that simulations faithfully reflect physical or operational reality. This article is relevant to open data because it highlights the need for rigorous standards in the generation and validation of data for simulation purposes. It promotes transparency in the quality of the data used, a fundamental principle that also underpins trust in open data ecosystems. The discussion on bias, representativeness, and continuous updates resonates with the accessibility and integrity objectives that define the philosophy of open data, ensuring that digital tools are inclusive and reliable.

Source: canal-ar.com.ar
Published on 2025-10-09

El futuro del testing de software: datos sintéticos & gemelos digitales

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