# A Systematic Mapping of Quality Models for AI Systems, Software and Components
## 2 Background of the Study
### AI and ML Software Components  
Several studies have componentized AI neural networks and ML models by exploiting reusability of component-based approaches. This has led to the rapid development of ML models, without requiring software engineers to have great knowledge of ML models and algorithms and also reducing maintenance complexity.  
[PERSON] et al. [20] computerized three neural network models, namely, the Multi-Layer Perceptron, the Learning Vector Quantization, and the Adaptive Resonance Theory family of networks. The authors adapted a component-based software engineering approach to neural network models to eliminate the tightly coupled solutions that end up in monolithic and non-reusable programming efforts. Furthermore, CompoNet (Componentized-neural-Networks) facilitates the development of neural network-based software systems by designing and implementing neural networks as software components. These components can be reused effortlessly across different applications, and thus, the shipping of trained models from the simulation to production environments is possible with minimal programming effort.  
In another area, building energy prediction plays an important role in steering design towards the required sustainability regulations. Several issues have been encountered in building energy prediction such as the time-consuming nature of detailed Building Energy Modelling (BEM), meta-models within the design process, and the lack of energy modelling experts in the early design stages. To overcome these issues, [PERSON] et al. [57] proposed component-based ML Modelling (MLM) for design-stage energy prediction. The component-based ML model was evaluated using weather data from Amsterdam, Brussels, and Paris. The results indicated that developing MLMs with diverse datasets and appropriate input parameters could result in models that generalize well under different design situations, provided that the new data match the distribution of the training data. Further research on the generalization of component-based MLM for building design with additional data and input parameters was performed to identify the full potential of such an approach.  
[PERSON] et al. [58] proposed a Component-Based ML (CBML) approach, specifically a deep learning architecture for Building Performance Simulations (BPS). BPS is a technology which helps to develop buildings that adhere to energy-efficiency requirements in high-performance building design and operation. The proposed CBML approach solved several existing issues in BPS, such as designers lacking fundamental knowledge of physical phenomena in BPS, making it challenging for them to understand simulation results and take appropriate design decisions. In addition, CBML helps to mitigate the time-consuming nature of simulating multiple design options using BPS. Moreover, the CBML approach plays an important role in the reusability of ML models for design situations that are different from the training situation. The study concluded that the proposed CBML approach supports the design process of buildings, especially in early phases. Components are plugged together as needed in the process, and prediction is performed immediately without additional modelling or significant computation time.