Understanding Source Code Maintainability Index - Static Code Analysis

Introduction to Maintainability

Maintainability is a crucial aspect of software engineering that measures how easily a software system or component can be modified to fix defects, improve performance, or adapt to a changed environment. High maintainability leads to lower costs and more efficient software evolution over time.

Sample Data and Metrics

We use several key metrics to evaluate maintainability:

1. Maintainability Index (MI): A measure of how maintainable the code is, with higher values indicating better maintainability.

2. Cyclomatic Complexity (CC): A measure of the complexity of the code, representing the number of linearly independent paths through the program.

3. Lines of Code (LOC): The number of lines in the source code, indicating the size of the codebase.

Below is a sample dataset with packages, files, classes, and variables:

| Package | File | Class | Method | Maintainability Index | Cyclomatic Complexity | Lines of Code |

|-----------------|-----------------|----------------------|-------------------|-----------------------|-----------------------|---------------|

| com.example.app | UserController | UserController | createUser | 68 | 16 | 130 |

| com.example.app | UserController | UserController | deleteUser | 93 | 0 | 1 |

| com.example.app | UserController | UserController | updateUser | 93 | 0 | 1 |

| com.example.app | OrderService | OrderService | processOrder | 75 | 20 | 200 |

| com.example.lib | Utility | StringUtil | capitalizeString | 88 | 5 | 50 |

Calculation and Interpretation

The simplest form of Maintainability Index (MI) is calculated using the following formula:

MI = 171 - 5.2 × log(Lines of Code) - 0.23 × Cyclomatic Complexity - 16.2 × log(Lines of Code)

Let's calculate the MI for the sample data:

1. UserController.createUser

MI = 171 - 5.2 x log(130) - 0.23 x 16 - 16.2 x log(130) ≈ 63.15

Original MI: 68

2. OrderService.processOrder

MI = 171 - 5.2 × log(200) - 0.23 × 20 - 16.2 × log(200) ≈ 70.24

Original MI: 75

(Actual values from the dataset may slightly differ due to additional factors in the real formula used by tools.)

Interpreting Maintainability Index

- High MI (> 90): Indicates very maintainable code. Focus on maintaining this standard.

- Medium MI (50-90): Indicates code that is maintainable but may need some improvements. Regular refactoring and code reviews can help.

- Low MI (< 50): Indicates poor maintainability. This code requires significant attention to improve its structure and readability.

Guidelines for Improvement

For packages, files, classes, and methods with an MI less than 90, focus on the following:

1. Reduce Cyclomatic Complexity: Simplify complex methods by breaking them into smaller, more manageable pieces.

2. Refactor Code: Regularly refactor code to improve readability and maintainability. Remove redundant code and improve structure.

3. Improve Documentation: Ensure that the code is well-documented to make it easier for other developers to understand and modify.

4. Automate Testing: Implement automated tests to ensure that changes do not introduce new defects.

Example Script Execution

Python script to calculate the Maintainability Index for the sample data:

## Make sure pandas and numpy are installed. 
import pandas as pd

import numpy as np

# Sample data

data = {

    'Package': ['com.example.app', 'com.example.app', 'com.example.app', 'com.example.app', 'com.example.lib'],

    'File': ['UserController', 'UserController', 'UserController', 'OrderService', 'Utility'],

    'Class': ['UserController', 'UserController', 'UserController', 'OrderService', 'StringUtil'],

    'Method': ['createUser', 'deleteUser', 'updateUser', 'processOrder', 'capitalizeString'],

    'Maintainability Index': [68, 93, 93, 75, 88],

    'Cyclomatic Complexity': [16, 0, 0, 20, 5],

    'Lines of Code': [130, 1, 1, 200, 50]

}

df = pd.DataFrame(data)

# Function to calculate Maintainability Index

def calculate_maintainability_index(cyclomatic_complexity, lines_of_code):

    if lines_of_code > 0:

        return 171 - 5.2  np.log(lines_of_code) - 0.23  cyclomatic_complexity - 16.2 * np.log(lines_of_code)

    else:

        return None

# Apply the calculation to each row

df['Calculated Maintainability Index'] = df.apply(

    lambda row: calculate_maintainability_index(row['Cyclomatic Complexity'], row['Lines of Code']),

    axis=1

)

# Display the DataFrame with original and calculated Maintainability Index

print(df[['Package', 'File', 'Class', 'Method', 'Maintainability Index', 'Calculated Maintainability Index']])        

This script calculates the Maintainability Index for each entry in the sample data and compares it with the original values.

Conclusion and Recommendations:

This document provides an understanding of maintainability, sample calculations, and guidelines for improving code maintainability. Use this as a reference to assess and improve the maintainability of your codebase.

Next steps:

a. Run the Python script to verify the calculations with your actual data.

b. Implement regular code reviews and refactoring sessions to continuously improve maintainability of code before your codebase is cluttered.

c. Learn more about the Code Quality Metrics -

• For usage and setup of code analysis metrics in Visual Studio
• How code metrics help identify risks - Visual Studio (Windows) | Microsoft Learn

#CodeMaintainability #CodeQuality #CodeMetrics #StaticAnalyis