Learn how to examine Complexity and Quality Metrics in DevOps

Learn how to examine Complexity and Quality Metrics in DevOps

Evaluating complexity and quality metrics is essential to understanding the maintainability, reliability, and efficiency of codebases. Below are key metrics and complexity measures used in software development to assess code quality and complexity.

1. Halstead Complexity Measures

Halstead complexity measures are used to quantify various aspects of code complexity:

1. Program Vocabulary (N1 and N2)

   • N1: Number of distinct operators.

   • N2: Number of distinct operands.

2. Program Length (N)

   • Program Length (N) = N1 + N2

   • Total count of unique operators and operands in the code.

3. Calculated Program Length (N’)

   • N’** = N \ log2(N)

4. Volume (V)

   • Volume (V) = N \ log2(N1 + N2)

   • Measures the overall complexity of the program.

5. Difficulty (D)

   • Difficulty (D) = (N1 / 2) \ (N2 / N1)

   • Represents the complexity of understanding and implementing code.

6. Effort (E)

   • Effort (E) = Difficulty \ Volume

   • Measures the amount of effort required to develop and maintain the software.

2. Common Quality-Related Metrics

Beyond complexity measures, other quality-related metrics provide insights into the performance and reliability of code:

1. Failed Builds Percentage

   • Indicates the proportion of builds that fail due to code issues.

   • Formula: (Number of failed builds / Total builds) \ 100

   • High percentages can suggest poor code quality or lack of testing.

2. Failed Deployments Percentage

   • Tracks the percentage of deployments that fail to go live.

   • Formula: (Number of failed deployments / Total deployments) \ 100

   • High values suggest issues with code stability or integration.

3. Ticket Volume

   • Number of tickets created for a codebase or sprint. Higher ticket volumes may indicate frequent bug reports or feature requests.

4. Bug Bounce Percentage

   • Percentage of bugs that are bounced back during QA or after development, typically due to code that doesn’t meet quality standards.

   • Formula: (Bounced bugs / Total bugs) \ 100

5. Unplanned Work Percentage

   • Tracks the percentage of work performed that was not originally planned or estimated.

   • High unplanned work suggests a lack of thorough initial design or overestimation of task complexity.

Using Halstead Complexity and Quality Metrics

Benefits of Using Complexity and Quality Metrics:

1. Early Detection: Helps identify areas in code that require attention early in the development lifecycle.

2. Resource Allocation: Assists in prioritizing work and allocating resources efficiently by identifying complex areas that require more effort.

3. Code Review: Provides quantitative measures for guiding code reviews and refactoring tasks.

4. Continuous Improvement: Enables teams to track improvements over time, reducing complexity and increasing code quality.

Application in Practice:

1. Automated Tools: Use tools like SonarQube, JIRA, or GitHub Insights to track these metrics.

2. Code Refactoring: Address high complexity measures through refactoring or improving test coverage.

3. CI/CD Pipelines: Include quality and complexity checks as part of CI/CD workflows.

Summary

Halstead complexity measures and quality-related metrics offer valuable insights into code quality. By assessing and optimizing these metrics, development teams can ensure that their software is maintainable, reliable, and efficient.