Agile Management for Machine Learning: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2506.20759v1
• Date: Wed, 25 Jun 2025 18:47:08 GMT
• Title: Agile Management for Machine Learning: A Systematic Mapping Study
• Authors: Lucas Romao, Hugo Villamizar, Romeu Oliveira, Silvio Alonso, Marcos Kalinowski,
• Abstract summary: Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations.<n>The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management.<n>This study aims to outline the state of the art in agile management for ML-enabled systems.
• Score: 1.0396117988046165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: [Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile methods, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems, where challenges require tailored approaches. [Goal] Our goal is to outline the state of the art in agile management for ML-enabled systems. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with backward and forward snowballing iterations. [Results] Our study identified 27 papers published between 2008 and 2024. From these, we identified eight frameworks and categorized recommendations and practices into eight key themes, such as Iteration Flexibility, Innovative ML-specific Artifacts, and the Minimal Viable Model. The main challenge identified across studies was accurate effort estimation for ML-related tasks. [Conclusion] This study contributes by mapping the state of the art and identifying open gaps in the field. While relevant work exists, more robust empirical evaluation is still needed to validate these contributions.

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