# Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/1908.02134 · HTML: https://mlsanity.com/paper/1908.02134/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems](https://mlsanity.com/paper/2303.02920/) (2023)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform](https://mlsanity.com/paper/2303.13151/) (2023)
- [AgentSLA : Towards a Service Level Agreement for AI Agents](https://mlsanity.com/paper/2511.02885/) (2025)
- [Quality Model for Machine Learning Components](https://mlsanity.com/paper/2602.05043/) (2026)

## Abstract (arXiv)

More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and textitEthics guidelines for trustworthy AI from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.