## 3 Results: Method Development, Implementation, and Testing of CADA Framework
### Method Summary and Explanation  
The CADA Method aims to create a human- and machine-readable dataset in the form of SN and GEs along a design workflow for a regulated industry that depends on transparent decision making and human evaluation.  
Figure 10 shows the synthesis of frameworks used to build the CADA Method. First, the design workflow with the four domains (Figure 10a) from AD assists the designer while performing a structured analysis of the design task, its decomposition into sub-tasks, and its solution. The connections between elements from various domains, illustrated as Cases (Figure 10b), reflect the crucial problem-solution relationships required for knowledge storage within Case-Based Reasoning (CBR) systems. Within a specific domain, recurring sets of information are integrated into Norms (see Figure 10c). The relationships between domain-specific datasets and Cases harbor essential information, as they constitute the AD-Matrix depicted in Figure 5. The colors assigned to these connections signify the quality of the design in relation to the independence axiom from AD. Within the framework of the CADA Method, this axiom suggests that Cases should remain independent of one another. Green connections (see Figure 10d) represent uncoupled cases, orange connections (see Figure 10e) indicate decoupled cases, and red connections (see Figure 10f) denote coupled designs.  
Figure 9: CBR with visual keys. (**a**) Method; (**b**) Example.  
The idea of \"zig-zagging\" in AD theory refers to the process of breaking down a specific task into explainable sub-elements. The CBR workflow can be used to efficiently decompose a design and its requirements along the AD domains on top of existing knowledge. In our method, key-value pairs, named Indices, are combined into a Norm (Figure 10c) to describe an information subset of a domain attribute, such as CR, FR, DP, or PV. This type of data structure enables the identification of similarities among combinations of Cases, Norms, and Domain Attributes within a CBR workflow. In the CBR cycle, the first action, called Retrieve, focuses on locating data in the case memory that corresponds to the given input. Customer requirements can result in a set of established and, therefore, reusable functional requirements, which are associated with design parameters and known process variables. In the subsequent revision phase, the designer evaluates the datasets, confirming acceptable elements, introducing new items, and adding any missing references. To complete the process, the updated dataset is saved in the case memory during the retention step.  
All information and references are formatted as depicted in Figure 10. The graphical representation of data, inspired by SN, offers a clear structure and guidance for designers. Any modifications to individual datasets reflect their impact on downstream domain items, facilitating visualization even during iterative or agile solution-finding processes.  
The necessary SN to create a comprehensive description from CR to PV can be quantified as a specific number of blocks (e.g., datasets) interconnected along the CADA workflow. In our design assistant, the complexity of the solutions can be articulated through the count of blocks and connections required to define the solution. When multiple solution paths are available, the designer should opt for the one that demands the least amount of description. This aligns with the second axiom, known as the Information Axiom, which asserts that the superior design is the one that can be conveyed with the least amount of information.