# A Systematic Mapping of Quality Models for AI Systems, Software and Components
## 3 Related Works  
This section lists down previous studies on AI-software and system quality. Their scope, objectives, and findings are compared with this SMS.  
[PERSON] and [PERSON] [5] performed a systematic literature review (SLR) of 29 papers on AI software quality. They discovered quality characteristics and their assurance, challenges, solutions, and existing quality models for AI-based software quality from 1988 to 2020. This present study aims to explore the quality of AI software, systems, and components together. The SMS is much broader compared to the SLR of [5], which only covered AI software quality, while this study aims to cover different quality levels of AI software, systems, and components.  
[PERSON] et al. [16] reviewed 72 papers on the development and maintenance of machine learning-based software systems, as well as difficulties and solutions. This research does not focus on product quality, and the software examined is ML-based.  
[PERSON] and [PERSON] [20] reviewed the current testing practices for ML-based systems. They found 37 linked main studies between 2007 and 2019 in the Engineering Village, Google Scholar, and Microsoft Academic Search databases. To ensure that ML programs were reliable, the authors looked for and found obstacles when testing such software systems, offered solutions advocated by research in the literature, and made recommendations based on their findings. This research intends to assist practitioners improve the quality of machine learning algorithms by teaching them how to test them. Unlike the earlier study, this paper focuses on different levels of AI software, systems, and components.  
[PERSON] et al. [59] conducted a systematic mapping study in the context of functional testing for machine learning systems (MLS), analyzing 70 papers. While this paper is interested in AI-based software, systems, and components, ref. [59] were more interested in MLS testing, difficulties, and solutions. While their research questions are focused on testing, the ones in this study are focused on AI software, systems, and component quality. As a result, the contributions and consequences of [59] differ significantly from the current study.  
Next, [PERSON] et al. [60] conducted a comprehensive survey study to examine ML software testing trends, difficulties, and solutions. They gathered 138 articles from 2007 to 2019 that focused on testing properties such as accuracy, robustness, efficiency, privacy, and fairness. They established and demonstrated existing machine learning testing methods and workflows, and evaluated quality attributes such as accuracy, robustness, and others as specific to testing of ML software systems, similar to the present study. The context and domain of [60], on the other hand, differ from this study, which aims to learn more about product quality than just software testing.  
Consequently, the shortcomings mentioned below have motivated the researchers to conduct a broader and more functional systematic mapping study:  
* From an analysis of the related literature, there is still a large gap in terms of addressing the quality of AI software, systems, and components.
* None of the former studies have explored this gap in the literature.
* Although previous research has increased our understanding of specific cases and contexts of AI-based software, it has not provided a clearer picture of AI software, systems, and component quality or of related challenges in experimental, academic, and industrial contexts.  
This paper analyses previous studies that propose quality models, together with their respective evaluation methods, quality characteristics and limitations. In my knowledge, none of them provides similar comprehensive SMS in the context of software quality for AI software, systems, and components. Finally, Table 1 highlights how this study differs from the previous ones.