# A Systematic Mapping of Quality Models for AI Systems, Software and Components
## 2 Background of the Study
### Software Component Quality Models
The study contains seven quality characteristics, which are functionality, reliability, usability, efficiency, maintainability, portability, and marketability, and each of them has sub-characteristics. Another study [48] presented a quality model aimed at evaluating COTS components, which was adapted from ISO 9126. The proposed model comprises quality characteristics and sub-characteristics. The model contains four quality characteristics, which are functionality, reusability, maintainability, and conformance. In [49], the authors introduced a quality model for component-based software which is derived from ISO 9126. The proposed model added several sub-characteristics to the ISO 9126 which are reusability, complexity, scalability, traceability, and flexibility. The study comprises six main characteristics, namely, functionality, reliability, usability, efficiency, maintainability, and portability. [PERSON] et al. [50] developed a quality model for software component evaluation based on ISO 9126. The proposed model contains eight main characteristics: functionality, reusability, portability, maintainability, usability, reliability, and modularity. [PERSON] et al. [51] identified quality characteristics for usability of software components, which were derived from the ISO 9126 standard. These characteristics were understandability, learnability, and operability. The study demonstrated that the observable elements of software components are documentation and some of the component's functional elements. [PERSON] et al. [52] conducted a survey on quality assurance and assessments for component-based systems. The study revealed that quality assurance and assessments in component-based systems are different from conventional software systems due to their hidden internal structures. The study also emphasized that delivering quality systems depends on the quality of components used. The authors highlighted several characteristics that contribute to the overall quality of a system, namely, performance, reliability, maintainability, and testability. The study also revealed a lack of automated tools in the literature for measuring quality characteristics. [PERSON] et al. [53] developed a reusability-based quality framework for software components. The aim of this study was to standardize the quality evaluation process, which helps stakeholders select software components that meet their requirements and application areas, where the stakeholders are users, managers, and developers. The proposed framework contains the main quality characteristics, with each having sub-characteristics. There were six main characteristics, namely, functionality, reliability, usability, efficiency, maintainability, and portability. [PERSON] et al. [54] introduced a quality assessment model for software components. Their proposed model contains eight primary characteristics, namely, design quality, plugability, reusability, functionality, reliability, maintainability, documentation quality, and efficiency. There were also thirty-three sub-characteristics in the model. The existing software component quality models, such as [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54], are not applicable to ML software because ML software is all about data [9]. Similarly, the study [55] highlightedthat the quality assurance of ML software differs from conventional software because ML software deals with data and also has characteristics quite different from software systems. Furthermore, the advances in AI technologies and data-driven ML techniques and in how to build high-quality AI software and applications has become a very hot subject [56]. For this reason, the special features of AI software bring new challenges and issues for validating software or system quality. Moreover, the specific quality characteristics of ML software are missing from the existing software component quality models. The existing studies on AI and ML software components are discussed in the next section.