# What is Software Quality for AI Engineers? Towards a Thinning of the Fog

arXiv: https://arxiv.org/abs/2203.12697 · HTML: https://mlsanity.com/paper/2203.12697/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Repairing Deep Neural Networks: Fix Patterns and Challenges](https://mlsanity.com/paper/2005.00972/) (2020)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [Quality Assurance for AI-based Systems: Overview and Challenges](https://mlsanity.com/paper/2102.05351/) (2021)
- [Towards Guidelines for Assessing Qualities of Machine Learning Systems](https://mlsanity.com/paper/2008.11007/) (2020)
- [Software engineering for artificial intelligence and machine learning software: A systematic literature review](https://mlsanity.com/paper/2011.03751/) (2020)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Quality Management of Machine Learning Systems](https://mlsanity.com/paper/2006.09529/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Using Quality Attribute Scenarios for ML Model Test Case Generation](https://mlsanity.com/paper/2406.08575/) (2024)

## Abstract (arXiv)

It is often overseen that AI-enabled systems are also software systems and therefore rely on software quality assurance (SQA). Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code. We conducted semi-structured interviews with representatives of ten Austrian SMEs that develop AI-enabled systems. A qualitative analysis of the interview data identified 12 issues in the development of AI/ML components. Furthermore, we identified when quality issues arise in AI/ML components and how they are detected. The results of this study should guide future work on software quality assurance processes and techniques for AI/ML components.