Identifying Concerns When Specifying Machine Learning-Enabled Systems: A Perspective-Based Approach

• Hugo Villamizar PUC-Rio
• Marcos Kalinowski PUC-Rio

DOI: https://doi.org/10.5753/cbsoft_estendido.2024.4131

Abstract

Engineering machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. This thesis presents PerSpecML, a perspective-based approach for specifying ML-enabled systems that helps practitioners identify which attributes are important to contribute to the overall system’s quality. We evaluated PerSpecML in three different contexts: (i) in academia, (ii) with industry representatives, and (iii) in two real industrial case studies. The results particularly revealed key components that would have been otherwise missed without using PerSpecML.

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Villamizar, H., Kalinowski, M., Lopes, H., and Mendez, D. (2024). Identifying concerns when specifying machine learning-enabled systems: a perspective-based approach. Journal of Systems and Software, 213:112053.