Automation of Insufficient MATLAB Input Parameters: Simplifying the Workflow with Tools and Scripts

# 1. The Challenge of Insufficient MATLAB Input Parameters

MATLAB programs require input parameters to provide the necessary information to complete specific tasks. However, when input parameters are insufficient, the program may encounter errors or produce unexpected results.

**1.1 The Impact of Insufficient Input Parameters on MATLAB Programs**

***Program Errors:** If essential input parameters are missing, MATLAB programs may throw errors, causing the program to fail to run normally.
***Unexpected Results:** If optional input parameters are absent, the program may use default values or produce results different from what is expected, potentially leading to inaccurate or misleading analyses.

# 2. Automating Input Parameter Handling with Tools

MATLAB offers a variety of tools and functionalities to help automate the handling of input parameters, thereby simplifying the development process and enhancing the robustness of the code.

### 2.1 MATLAB Input Parameter Validation Toolbox

The MATLAB Input Parameter Validation Toolbox is a powerful tool that allows users to define and apply validation rules to check the validity of input parameters.

#### 2.1.1 Creating and Applying Validation Rules

To create validation rules, you can use the `validationRule` function. This function accepts a validation function as an argument that returns a boolean value indicating whether the input is valid. For instance, to verify if the input is a positive number, you can use the following validation function:

function isValid = isPositive(input)
     isValid = input > 0;
end

Once the validation rule is created, you can use the `validateattributes` function to apply the rule to input parameters. `validateattributes` accepts the following arguments:

* Input parameter
* Validation rule
* Error message (optional)

For example, to verify if the input parameter `x` is a positive number, you can use the following code:

x = 10;
validateattributes(x, {'numeric'}, {'scalar', 'positive'}, 'myFunction', 'x');

If `x` is not positive, the `validateattributes` function will throw an error and display the specified error message.

#### 2.1.2 Error Handling When Parameters Are Missing

The MATLAB Input Parameter Validation Toolbox also provides a mechanism for handling missing parameters. When using the `validateattributes` function, you can specify a default value to be used if an input parameter is missing. For example, to set the default value of `x` to 1, you can use the following code:

x = 10;
validateattributes(x, {'numeric'}, {'scalar', 'positive'}, 'myFunction', 'x', 1);

If `x` is missing, the `validateattributes` function will use the default value of 1.

### 2.2 Code Generator

The MATLAB code generator can automatically generate MATLAB functions from Simulink models. The generated functions include code for validating input parameters, ensuring the robustness of the function.

#### 2.2.1 Generating MATLAB Functions from Simulink Models

To generate MATLAB functions from Simulink models, you can use the `codegen` function from the `Simulink.codegen` package. This function accepts a Simulink model as a parameter and generates a MATLAB function file. For example, to generate a MATLAB function from the `myModel.slx` model, you can use the following code:

codegen('myModel.slx');

#### 2.2.2 Automatically Generating Input Parameter Validation Code

The MATLAB