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.
To measure and quantify technical debt using data, see the BigQuery ML for Technical Debt Analysis guide. For tracking tech debt reduction through engineering metrics, the DORA and SPACE Metrics framework provides valuable signal.
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¶
Shortcut Accumulation: Low code quality typically results from engineers taking tactical shortcuts or making architectural compromises during development. This manifest as code duplication, excessive complexity, and non-adherence to established standards.
Maintenance Velocity Latency: Over time, these tactical compromises accumulate, making the codebase exponentially harder to maintain. The engineering team begins spending a disproportionate amount of time working around legacy defects rather than delivering high-value features.
Strategic Business Impact: Unmanaged technical debt significantly slows release cycles, increases total cost of ownership (TCO), and negatively impacts overall product reliability. This can lead to stakeholder dissatisfaction and a loss of competitive advantage.
Measuring code quality has several benefits, including¶
Early Defect Detection: Measuring code quality allows for the identification of potential failures early in the lifecycle, enabling remediation before they become prohibitively complex.
Architectural Maintainability: High-quality code is naturally easier to understand and extend, reducing the engineering overhead required for future roadmap delivery.
Strategic Debt Minimisation: By maintaining rigorous quality standards, organisations can minimise the accumulation of debt, ensuring a more resilient and scalable product.
Increased Engineering Velocity: High code quality allows developers to work more efficiently, leading to faster development cycles and improved time-to-market.
Enhanced Team Collaboration: Adhering to unified coding standards ensures that the codebase remains accessible to every team member, fostering a culture of shared ownership.
Superior Product Reliability: Robust code leads to improved system stability, resulting in higher customer satisfaction and a sustainable competitive advantage.
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.
Static Code Analysis: Leveraging automated tools to identify duplications, architectural complexity, and potential bugs. This provides a quantitative baseline for the health of the codebase.
Tactical Code Reviews: Regular peer reviews allow senior engineers to identify subtle design flaws and technical debt accumulation that automated tools may miss.
Historical Data Synthesis: Analysing bug density, commit frequency, and lead times to identify high-interest debt clusters that require immediate attention.
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.
Automated Test Coverage: The percentage of the codebase covered by unit and integration tests. Low coverage is a primary indicator of high-risk technical debt.
Cyclomatic Complexity: A quantitative measure of the number of linearly independent paths through the source code. Higher complexity directly correlates with increased defect density.
Iterative Code Churn: The frequency with which specific code segments are modified. High churn in legacy components often indicates architectural instability.
Preventing Technical Debt¶
To prevent technical debt, consider the following best practices.
Adherence to Global Standards: Enforcing rigorous coding guidelines and architectural standards from the first commit.
Continuous Peer Review: Ensuring that every code change is scrutinised for quality and long-term maintainability.
Comprehensive Test Automation: Implementing a "Shift-Left" testing strategy to catch defects before they enter the main trunk.
Proactive Architectural Refactoring: Encouraging engineers to leave the code better than they found it by resolving minor debt during feature development.
Strategic Debt Prioritisation: Including technical debt reduction as a standard component of every sprint and project planning cycle.
Addressing Technical Debt¶
After estimating technical debt, you can take the following steps to address it:
Critical Issue Prioritisation: Identifying and resolving high-interest debt that directly impacts user experience or delivery velocity.
Dedicated Resource Sprints: Allocating specific cycles for repayment of architectural debt, ensuring it doesn't become a permanent tax on development.
Surgical Code Refactoring: Systematically improving problematic code segments to enhance maintainability and reduce systemic complexity.
Continuous Regression Monitoring: Enhancing automated test suites after debt repayment to ensure that improvements are preserved.
Culture of Quality Mastery: Fostering an environment where addressing technical debt is viewed as a foundational engineering responsibility.
Financial aspect of the Technical Debt¶
Understanding the financial aspect of technical debt is a foundational component of modern FinOps practices. 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.