Applying a Requirements-Focused Agile Management Approach for Machine Learning-Enabled Systems
Lucas Romao, Luiz Xavier, J\'ulia Cond\'e Ara\'ujo, Marina Cond\'e Ara\'ujo, Ariane Rodrigues, and Marcos Kalinowski

TL;DR
This paper presents RefineML, a requirements-focused agile approach tailored for ML-enabled systems, demonstrating high acceptance and practical benefits in industry-academia collaboration, while addressing unique challenges of ML integration.
Contribution
It introduces RefineML, a novel approach integrating ML-specific requirements with agile practices, and provides empirical evidence of its effectiveness in real-world projects.
Findings
High perceived usefulness and acceptance among stakeholders
Improved communication and early feasibility assessments
Enables dual-track governance for ML and software development
Abstract
Machine Learning (ML)-enabled systems challenge traditional Requirements Engineering (RE) and agile management due to data dependence, experimentation, and uncertain model behavior. Existing RE and agile practices remain poorly integrated and insufficiently tailored to these characteristics. This paper reports on the practical experience of applying RefineML, a requirements-focused approach for the continuous and agile refinement of ML-enabled systems, which integrates ML-tailored specification and agile management approaches with best practices derived from a systematic mapping study. The application context concerns an industry-academia collaboration project between PUC-Rio and EXA, a Brazilian cybersecurity company. For evaluation purposes, we applied questionnaires assessing RefineML's suitability and overall acceptance and semi-structured interviews. We applied thematic analysis to…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Engineering Research · Big Data and Business Intelligence
