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Technical Debt Ratio: Why & How to Calculate

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Last updated on
October 10, 2024

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Technical Debt Ratio: Why & How to Calculate

What is Technical Debt Ratio

Technical Debt Ratio is a metric that provides insights into the quality of code in a software project by comparing the cost to fix the technical debt against the total cost of developing the code. It helps teams and management to understand the proportion of the development effort that is consumed by technical debt and provides a high-level overview of the codebase's health.

How to measure Technical Debt Ratio

Formula for measuring Technical Debt Ratio:

TDR=(Cost to Fix Technical Debt/Total Development Cost)×100%

Components:

  • Cost to Fix Technical Debt: The estimated effort required to fix all known issues and code smells in the codebase, often measured in person-days or person-hours.
  • Total Development Cost: The total effort spent on developing the code, which can include designing, coding, testing, and deploying the software.

Why to measure Technical Debt Ratio

Legacy modernization challenges - technical debt
Report: State of Software Modernization 2024
  • Quantifying Technical Debt: TDR quantifies technical debt in a way that can be understood by both technical and non-technical stakeholders, facilitating discussions and decision-making related to technical debt management.
  • Prioritization: By understanding the TDR, teams can prioritize refactoring efforts and allocate resources more effectively to manage technical debt.
  • Quality Insight: A higher TDR indicates a higher proportion of development effort is being spent on managing technical debt, which may suggest lower code quality.
  • Risk Management: TDR can be used to assess the risk associated with technical debt and to make informed decisions about when to address technical debt versus when to focus on feature development.

When & How to Use Technical Debt Ratio Metric

  • Continuous Monitoring: TDR should be monitored regularly to track the evolution of technical debt over time and to identify trends or issues that may need to be addressed.
  • Strategic Planning: It can be used in strategic planning and backlog prioritization to ensure that technical debt is addressed in a timely and cost-effective manner.
  • Communication: TDR can be used to communicate the impact and importance of technical debt to stakeholders and to justify investments in code quality and refactoring.

Limitations of Technical Debt Ratio

  • Estimation Accuracy: The accuracy of TDR is dependent on the accuracy of the estimates for the cost to fix technical debt and the total development cost, which can be challenging to determine precisely.
  • Not a Standalone Metric: While TDR provides valuable insights, it should be used alongside other metrics and qualitative assessments to provide a comprehensive view of code quality and technical debt.

Technical Debt Ratio on new code

Technical Debt Ratio (TDR) on new code refers to the proportion of technical debt introduced relative to the new code that has been developed over a specific period or for a particular release. This metric helps teams to monitor and manage the quality of the code being produced and to ensure that technical debt is kept under control as the codebase evolves.

Why to measure Technical Debt Ratio on new code

  • Quality Control: TDR on new code helps to ensure that the quality of the code being produced meets the team’s standards and that technical debt is not accumulating unchecked.
  • Early Intervention: By monitoring technical debt in new code, teams can identify and address issues early, before they become embedded in the codebase.
  • Continuous Improvement: It allows teams to assess the effectiveness of practices and tools in managing code quality and to identify areas for improvement.
  • Balanced Development: Ensures that the focus is not only on feature development but also on maintaining a healthy codebase.

When it's useful

  • Sprint Retrospectives: TDR on new code can be reviewed during sprint retrospectives to discuss the quality of the code produced and to identify any trends or issues.
  • Release Planning: It can be used in release planning to ensure that technical debt is considered and managed effectively throughout the development process.
  • Quality Assurance: It can be used as a quality assurance metric to ensure that the code being produced adheres to the team’s quality standards.

Limitations of Technical Debt Ratio on new code

  • Estimation Challenges: Accurately estimating the cost to fix technical debt and the cost to develop new code can be challenging and may impact the accuracy of the TDR.
  • Contextual Understanding: TDR on new code should be understood in the context of the project, as a higher TDR may be acceptable in some cases (e.g., prototyping, proof of concept) but not in others.

Practical Implications:

  • Refactoring: If the TDR on new code is consistently high, it may indicate that refactoring and technical debt management need to be prioritized.
  • Process Improvement: It may highlight areas where the development process can be improved to prevent the introduction of technical debt (e.g., better requirements definition, improved testing practices).
  • Team Discussions: It provides a basis for discussions within the team about code quality, technical debt, and development practices.

Case study - Technical Debt Ratio x Payment processing app

To illustrate the concept of measuring technical debt ratio in a fintech app, let's consider a hypothetical example.

Identifying Technical Debt Components:

  1. Codebase Analysis: The development team reviews the codebase and identifies areas with outdated libraries, redundant code, and inefficient algorithms. For instance, they find that the payment processing module uses an outdated encryption library.
  2. Architecture Review: The team notices that the app's microservices architecture is not optimally designed, leading to frequent downtime during high traffic.
  3. Documentation and Code Comments: The documentation is outdated, and there are very few code comments, making it difficult for new developers to understand the code.

Quantifying Technical Debt:

  1. Time Estimation for Refactoring: The team estimates that it would take approximately 200 hours to update the encryption library, 500 hours to optimize the microservices architecture, and 100 hours to update documentation and add necessary code comments.
  2. Financial Cost: They calculate the cost based on the hourly rate of developers. Suppose the rate is $50/hour, the total cost for refactoring would be: (200+500+100)×50=$40,000(200+500+100)×50=$40,000.

Calculating Technical Debt Ratio:

  1. Project Cost: Assume the overall cost of building the fintech app was $500,000.
  2. Technical Debt Ratio Calculation: The technical debt ratio is calculated by dividing the cost of resolving technical debt by the total project cost. So, in this case, it would be: 40,000500,000=0.08500,00040,000​=0.08 or 8%.

This 8% technical debt ratio indicates that 8% of the project's budget would be required to pay off the existing technical debt. This metric helps in understanding the scale of the debt and in making informed decisions about whether and when to address it.

Start measuring Technical Debt Ratio

Technical Debt Ratio is a valuable metric for understanding and managing technical debt, facilitating strategic planning, and communicating the importance of technical debt management to stakeholders. However, it should be used as part of a broader strategy for managing technical debt and maintaining code quality.

Technical Debt Ratio on new code is a vital metric for managing code quality in an ongoing development project, ensuring that technical debt is identified and addressed promptly, and maintaining a healthy, sustainable codebase.

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Authors

Olga Gierszal
github
IT Outsourcing Market Analyst & Software Engineering Editor

Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.

Olga Gierszal
github
IT Outsourcing Market Analyst & Software Engineering Editor

Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.

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