Technical Debt¶

Estimated time to read: 7 minutes

Technical Debt is a concept in software development that refers to the implied cost of additional work caused by using a shortcut solution now, instead of taking the time to implement a better and more sustainable solution. In essence, it is the long-term consequences of poor design, architecture, or code quality that can slow down future development, increase maintenance efforts, and decrease the overall quality of a software product.

Technical debt is closely related to engineering code quality. Code quality refers to how well the source code adheres to best practices, coding standards, and design principles. High-quality code is easier to maintain, understand, and extend, while low-quality code often leads to technical debt.

The relationship between technical debt and code quality works as follows¶

When code quality is low, it usually means that developers have taken shortcuts or made compromises during development. This may result in issues such as code duplication, high complexity, or lack of adherence to coding standards.
Over time, these issues accumulate and make the codebase harder to maintain and extend, leading to an increase in technical debt. As a result, the development team spends more time fixing issues, working around problems, and maintaining the codebase instead of focusing on new features.
Technical debt can slow down development, increase maintenance costs, and negatively impact the overall quality of the software product. This may result in customer dissatisfaction, reduced sales, and a loss of competitive advantage.

Measuring code quality has several benefits, including¶

Early detection of issues: By measuring code quality, you can identify potential problems early in the development process, allowing developers to fix issues before they become more complex and costly.
Improved maintainability: High-quality code is easier to maintain and extend, reducing the time and effort required for future development and maintenance tasks.
Reduced technical debt: By maintaining high code quality, you can minimise the accumulation of technical debt, resulting in a more robust and reliable software product.
Increased development speed: With higher code quality, developers can work more efficiently, leading to faster development cycles and quicker time-to-market for new features.
Enhanced team collaboration: Adhering to coding standards and best practices can improve team collaboration, as the code is easier to understand and work on for all team members.
Better product quality: High-quality code typically leads to more stable and reliable software, resulting in increased customer satisfaction and a competitive advantage for your company.

To measure code quality, you can use a combination of static code analysis tools, code reviews, and code quality metrics such as code coverage, cyclomatic complexity, and code churn. By monitoring code quality and addressing issues proactively, you can reduce technical debt and its associated costs, while improving the overall quality and maintainability of your software product. In the next paragraphs, I'm elaborating on these topics and tools.

Estimating Technical Debt¶

Estimating technical debt can be challenging, as it requires a comprehensive understanding of the codebase and the potential issues associated with it. Here are a few ways to estimate technical debt.

Code Analysis: Use static code analysis tools to identify code quality issues, like duplications, code complexity, and potential bugs. These tools can provide insights into the overall quality of the codebase and help quantify technical debt.
Code Review: Conduct regular code reviews with experienced team members who can spot potential problems and evaluate the quality of the codebase. This can help in identifying areas where technical debt exists or may accumulate.
Historical Data: Analyze historical data, such as bug reports, code commits, and development time, to identify trends and areas where technical debt may be accumulating.

Measuring Technical Debt¶

The next step after estimating the technical debt you have is to quantify it. There isn't a universal formula to measure technical debt, as it depends on various factors. However, some common metrics can be used to assess the overall health of a codebase and the potential technical debt.

Code Coverage: The percentage of the codebase covered by automated tests. Low code coverage may indicate a high risk of technical debt.
Code Complexity: Measure code complexity using metrics like Cyclomatic Complexity, which evaluates the number of linearly independent paths through the source code.
Code Churn: The rate at which code is being churned or changed over time. A high code churn may indicate that the codebase is frequently being altered, which can lead to technical debt.

Preventing Technical Debt¶

To prevent technical debt, consider the following best practices.

Focus on code quality by adhering to coding standards and guidelines.
Conduct regular code reviews to identify potential issues early.
Implement automated testing to ensure the codebase is thoroughly tested and maintainable.
Encourage refactoring to improve the code quality and reduce complexity.
Plan and prioritise technical debt reduction efforts during regular project planning.

Addressing Technical Debt¶

After estimating technical debt, you can take the following steps to address it:

Prioritize: Identify and prioritise the most critical issues that need to be addressed to reduce technical debt.
Allocate Resources: Dedicate time and resources to address technical debt during each development cycle.
Refactoring: Refactor problematic code, improving its quality and making it more maintainable.
Test and Monitor: Implement and improve automated testing to ensure that changes made to address technical debt do not introduce new issues.
Continuous Improvement: Encourage a culture of continuous improvement, where addressing technical debt is an ongoing effort rather than a one-time activity.

Financial aspect of the Technical Debt¶

The technical debt is linked directly to monetary cost and extra working hours required to remediate. You can use formulas and statistical models to help estimate and measure the financial impact of technical debt. However, the accuracy of these estimates largely depends on the quality and completeness of your data. Here are some formulas and methods you can use:

Cost of additional development time¶

Cost = (Average Hourly Developer Rate) * (Additional Hours Spent Due to Technical Debt)

To estimate the additional hours spent due to technical debt, you may use historical data, expert judgment, or regression models that predict development time based on factors like code complexity, code churn, and team size.

Cost of increased maintenance¶

Cost = (Average Hourly Maintenance Rate) * (Additional Hours Spent on Maintenance Due to Technical Debt)

To estimate the additional maintenance hours, you may use historical data, expert judgment, or develop regression models that predict maintenance time based on factors like the number of defects, system failures, and code quality metrics.

Impact on sales and customer satisfaction¶

Lost Revenue = (Number of Dissatisfied Customers) * (Average Revenue per Customer)

To estimate the number of dissatisfied customers, you can use customer feedback data and sentiment analysis techniques. You may also build regression or classification models that predict customer churn or satisfaction based on variables such as product quality, release delays, and other factors related to technical debt.

Total financial impact¶

Total Financial Impact = (Cost of Additional Development Time) + (Cost of Increased Maintenance) + (Lost Revenue)

While these formulas can provide a rough estimate of the financial impact of technical debt, it is essential to remember that the actual impact may vary depending on various factors. Collecting and analysing more data, using sophisticated statistical models, and continuously refining your estimates can help improve the accuracy of your calculations.

In addition to these formulas, you may also consider using techniques like Monte Carlo simulations to account for uncertainties in your estimates or employing time series analysis to forecast the future impact of technical debt on your company's finances.

By addressing technical debt proactively, you can improve the overall quality of your software, reduce maintenance costs, and make future development faster and more efficient.

Bonus¶

Find an example of code that you maybe want to develop to estimate your Technical debt using BigQuery. That code example is only for experimentation.