A QUICK SUMMARY – FOR THE BUSY ONES
TABLE OF CONTENTS
Utilizing metrics allows teams to track the impact of technical debt on software quality, productivity, and maintenance costs over time, enabling them to make informed decisions about resource allocation and debt management strategies.
Code churn refers to the percentage of a developer's own code that is recently edited (added, modified, deleted) after being written.
Code churn can be indicative of indecision, lack of clarity, or misunderstanding – all of which contribute to technical debt. High code churn, especially late in a development cycle, can be a red flag that code may be less reliable or harder to maintain.
When to Use: Use this metric during the development phase and after releases to identify unstable code.
How to Measure: Calculate the percentage of lines of code that have been changed, added, or deleted during a specific time frame.
How it Helps: Identifying areas of frequent changes helps to pinpoint unstable code and prevent potential future issues related to constant modifications.
Analyzing code churn alongside bug count can provide insights into whether the churn is constructive (refactoring, improving code) or destructive (introducing more defects).
TDR is a measure of the cost to fix the technical debt relative to the size of the codebase.
TDR provides a quick snapshot of the quality of code being produced and helps to quantify technical debt in economic terms, making it easier to communicate the impact and necessity of addressing technical debt to management.
When to Use: Use TDR to assess and communicate the overall health of the codebase to both technical and non-technical stakeholders.
How to Measure: TDR = (Cost to fix debt / Development cost) * 100%.
How it Helps: It provides a high-level overview of the code quality and helps prioritize refactoring efforts by identifying critical areas of debt.
Regularly monitoring TDR allows teams to observe trends and understand whether their code quality and technical debt are improving or deteriorating over time.
This metric indicates the percentage of duplicated code across the codebase.
Code duplication often leads to maintenance challenges. If a bug is found in a piece of duplicated code, it needs to be fixed in all instances. This increases the likelihood of missed defects and future issues.
When to Use: Use it continuously to ensure that the codebase remains DRY (Don't Repeat Yourself).
How to Measure: Analyze the codebase using static code analysis tools to identify and quantify duplicated blocks of code.
How it Helps: Reducing code duplication enhances maintainability and reduces the likelihood of introducing inconsistencies during updates.
Tracking code duplication over time and across projects can highlight systemic issues in development practices that may need to be addressed.
It measures the number of linearly independent paths through a program’s source code, indicating code complexity.
High cyclomatic complexity indicates that the code may be harder to test thoroughly and more prone to defects. It can also be harder to read and understand, increasing the likelihood of introducing errors during future changes.
When to Use: Use this metric to identify complex code that may be harder to maintain and more prone to defects.
How to Measure: Use static analysis tools to calculate the number of independent paths through the code.
How it Helps: Managing complexity ensures that the code remains maintainable and reduces the risk of introducing errors during changes.
Analyzing cyclomatic complexity at a method, class, and module level can help identify specific areas that may benefit most from refactoring.
The quadrant categorizes technical debt into four types: Reckless vs. Prudent and Deliberate vs. Inadvertent.
Understanding the nature and origin of technical debt helps teams to develop strategies for addressing it and potentially preventing similar debt in the future. It also aids in making informed decisions about when to incur deliberate debt.
When to Use: Use this metric during project retrospectives and planning to understand and communicate the nature of the technical debt.
How to Measure: Categorize existing technical debt items into the four quadrants based on retrospectives and team discussions.
How it Helps: It helps in understanding the origin of debt and informs strategies for prevention and repayment.
Regularly revisiting and re-evaluating the technical debt quadrant helps to ensure that deliberate and prudent debt does not inadvertently become reckless or inadvertent over time.
This metric tracks the number of open bugs and how long they have been open.
A growing number of open bugs or bugs that have been open for a long time can indicate that technical debt is being neglected. This can lead to a deteriorating codebase and user experience.
When to Use: Use this metric continuously to monitor the overall quality and health of the application.
How to Measure: Use issue tracking tools to count the number of open bugs and calculate their age.
How it Helps: Ensuring bugs are addressed in a timely manner prevents the accumulation of defect-related technical debt.
Analyzing bug count and age alongside code churn and TDR can provide a more comprehensive view of the codebase's health and the impact of technical debt.
Test coverage indicates the percentage of your codebase tested by automated tests.
While high test coverage is not a guarantee of code quality, inadequate test coverage can mean that the codebase is not well-protected against regressions, making it riskier to address technical debt through refactoring.
When to Use: Use this metric continuously to ensure that the codebase is adequately tested.
How to Measure: Use testing tools and frameworks to calculate the percentage of code executed during testing.
How it Helps: Maintaining adequate test coverage ensures that the codebase is robust and allows for safer refactoring and feature development.
Observing test coverage alongside bug discovery rates can help teams understand whether their tests are effective and where additional testing may be beneficial.
TTM is the time taken to develop and deploy a feature or product from conception to the customer.
If TTM is increasing, it may indicate that technical debt is slowing down feature development. This can impact the organization’s ability to compete and respond to market demands.
When to Use: Use this metric to assess the impact of technical debt on feature development speed.
How to Measure: Measure the duration from when a feature is planned to when it is available to users.
How it Helps: Monitoring TTM helps to understand how technical debt is impacting the team’s ability to deliver features promptly.
Analyzing TTM alongside other metrics like code churn and open bug count can provide insights into whether increasing TTM is due to technical debt or other factors.
Each of these metrics provides a different lens through which to view technical debt, and together they offer a comprehensive overview of the health of the codebase and the impact of technical debt on the team and the product. It's crucial to analyze these metrics holistically and in the context of one another to develop effective strategies for managing technical debt.
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