Defesa de Dissertação de Mestrado do aluno Antonio Pedro Santos Alves

Defesa de Dissertação de Mestrado do aluno Antonio Pedro Santos Alves.

Título da dissertação: Requirements Engineering for ML-Enabled Systems: Status Quo and Problems

Resumo: Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products, services, and processes. Literature suggests that Requirements Engineering (RE) can help to address many problems when engineering ML-Enabled Systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantita-tive statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involv-ing open and axial coding procedures. We found significant differences in RE practices within ML projects, some of them have been reported on literature and some are totally new. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.

Orientador: Prof. Dr. Marcos Kalinowski

Co-orientador: Prof. Dr. Daniel Mendez

Prof. Dr. Hélio Côrtes Vieira Lopes

Profª. Dra. Maria Teresa Baldassarre


Assista a defesa pelo link: https://puc-rio.zoom.us/j/4666190940?pwd=eUdNaDNSbnhEY3VWWU1DMGF0SkRjZz09