Halstead volume is part of a set of software metrics introduced by Maurice Howard Halstead in 1977. Similar to other halstead complexity measures, Halstead volume takes into account the number of operators and operands and aims to describe the size of the implementation of the module or the algorithm. It is represented by the following formula:
V = N * log2(n)
V = Halstead volume,
N = Program length,
And n = Program Vocabulary.
Program Length = N = N1 + N2 = total number of operators + total number of operands
Program vocabulary = n = n1 + n2 = number of operators + number of operands
Maintainability Index or MI is a score of how easy it is to maintain code. It is a combination of the four metrics: Cyclomatic complexity and Halstead volume, Lines of Code (LoC) and depth of inheritance. This is considered to be a metric that helps give an overall picture of complexity as it weighs Halstead volume and cyclomatic complexity against LoC and depth of inheritance. The traditional formula is defined as follows:
Maintainability Index = 171 - 5.2 * ln(Halstead Volume) - 0.23 * (Cyclomatic Complexity) - 16.2 * ln(Lines of Code)
Since the above formula results in a range of [-∞, 171], a slightly modified formula is used to bind the range of MI to [0, 100]:
Maintainability Index = MAX(0,(171 - 5.2 * ln(Halstead Volume) - 0.23 * (Cyclomatic Complexity) - 16.2 * ln(Lines of Code))*100 / 171)
Ideally, an MI of less than 10 is considered good, while 10 to 19 is acceptable, and scores >20 are considered high priority for rework.
Benefits of Measuring Code Complexity
Measuring code complexity is an important aspect of software development that offers several benefits. Here are some key benefits of measuring code complexity:
Better Code Quality: Measuring code complexity allows developers to identify potential problems early in development. This, in turn, helps to improve code quality by ensuring that issues are addressed before the code is deployed in production. Code complexity metrics can also be used to set targets for code quality, which can help developers focus on writing cleaner, more maintainable code.
Improved Maintainability: Code complexity directly relates to the ease with which code can be maintained and updated. By measuring code complexity, developers can identify areas of the code that are difficult to maintain and improve. This can help reduce the cost of maintaining the code over time.
Reduced Bugs: Complex code is more prone to errors and bugs. By measuring code complexity, developers can identify areas of the code that are likely to cause problems and focus their testing efforts accordingly. This can reduce the number of bugs and improve the overall reliability of the code.
Faster Development: Measuring code complexity can also help to speed up development. By identifying areas of the code that are particularly complex, developers can focus on simplifying these areas, which can reduce the amount of time required to write, test and deploy the code.
Improved Collaboration: Code complexity metrics can identify areas of the code that are particularly complex and require additional expertise. This can help improve collaboration between team members, as developers with specific skills can be brought in to help with particularly complex code areas.
Better Code Documentation: By measuring code complexity, developers can identify areas of the code that are particularly complex and require additional documentation. This can help ensure the code is well-documented and easier to understand for other developers needing to work with it in the future.
Measuring code complexity is an important part of software development that can help to improve code quality, reduce bugs, improve maintainability, speed up development, and improve collaboration between team members.
What to do about code complexity?
Code complexity is an indicator of code quality and its ease of maintenance. Complexity directly impacts delivery times and the quality of products shipped by a team. Since products are usually maintained over a long time period, teams should strive to optimize complexity in order to allow long-term ease of use, readability, and maintenance of code.
Optimizing for complexity involves a careful study of the current patterns and setting of baselines and acceptable standards based on the observed patterns and industry standards. Metrics such as cycle time, throughput, review practice, focus time, communication patterns, etc., help engineering leaders measure and optimize code quality.
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