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

arXiv: https://arxiv.org/abs/2309.07980 · HTML: https://mlsanity.com/paper/2309.07980/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [Naming the Pain in Machine Learning-Enabled Systems Engineering](https://mlsanity.com/paper/2406.04359/) (2024)
- [Using Quality Attribute Scenarios for ML Model Test Case Generation](https://mlsanity.com/paper/2406.08575/) (2024)
- [Define-ML: An Approach to Ideate Machine Learning-Enabled Systems](https://mlsanity.com/paper/2506.20621/) (2025)

## Abstract (arXiv)

Engineering successful machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. Among those challenges are how to effectively address unrealistic expectations of ML capabilities from customers, managers and even other team members, and how to connect business value to engineering and data science activities composed by interdisciplinary teams. In this paper, we present PerSpecML, a perspective-based approach for specifying ML-enabled systems that helps practitioners identify which attributes, including ML and non-ML components, are important to contribute to the overall system's quality. The approach involves analyzing 59 concerns related to typical tasks that practitioners face in ML projects, grouping them into five perspectives: system objectives, user experience, infrastructure, model, and data. Together, these perspectives serve to mediate the communication between business owners, domain experts, designers, software and ML engineers, and data scientists. The creation of PerSpecML involved a series of validations conducted in different contexts: (i) in academia, (ii) with industry representatives, and (iii) in two real industrial case studies. As a result of the diverse validations and continuous improvements, PerSpecML stands as a promising approach, poised to positively impact the specification of ML-enabled systems, particularly helping to reveal key components that would have been otherwise missed without using PerSpecML.