Tech debt, also known as technical debt, is a term used to describe the cost of maintaining a software system over time. It refers to the trade-off made between short-term gains in delivering software quickly and the long-term costs of maintaining and fixing issues that arise as a result. This is a common issue in software engineering and can have serious implications for a company’s bottom line.
The debt metaphor was coined by Ward Cunningham. Here is the small video talk where he discussed this metaphor
Classifying Tech Debt
- Architecture Debt
High-level design issues, such as scalability, maintainability & extensibility. Architecture debt is typically more expensive to fix than code debt, as it involves making changes to the overall design of a system, which can have far-reaching consequences. - Code Debt
Refers to the cost of maintaining a codebase due to quick and dirty solutions and poor initial design choices. It refers to issues within the code itself, such as poor quality, lack of modularity, or duplication. - Developer Efficiency Debt
Refers to the cost of not having the right tests, monitoring, or alerting in place, affecting developer productivity - Stability Debt
Refers to the accumulated tech debt that affects the stability of the infrastructure - Security Debt
Refers to security vulnerabilities in the technology stack - Documentation and Maintenance Debt
Refers to the cost of updating or creating documentation, as well as keeping up with updates and upgrades to technology work - Technical Product Debt
Refers to the visible negative impact of tech debt on the product. - Infrastructure Debt
Refers to the cost of fixing or upgrading outdated technology infrastructure.
Metrics for Tech Debt
Measuring Tech Debt is important in order to understand its impact and prioritize its resolution. Here are some common metrics that can be used to measure Tech Debt:
- Defect Density
Number of defects divided by the number of lines of code (LOC). For example, if you have 1000 LOC and 20 defects, your defect density is 20/1000 = 0.02. - Code Complexity
There are several methods for measuring code complexity, including Cyclomatic Complexity, Maintainability Index, and Code Duplication. For example, Cyclomatic Complexity measures the number of independent paths through a program, and can be calculated using a formula that considers the number of branches, loops, and decisions in the code. - Time-to-Market
The time elapsed from the start of development to the release of a new product or feature. For example, if development starts on January 1st and the product is released on March 1st, the Time-to-Market is 60 days. - Maintenance Costs
The cost of fixing or maintaining existing code or systems. This can include the cost of fixing bugs, making changes to the codebase, and updating documentation. For example, if the cost of fixing a bug is $1000, the maintenance cost for that bug is $1000. - Technical Debt Ratio
The ratio of the cost of fixing technical debt to the cost of developing new features. For example, if the cost of fixing technical debt is $100,000 and the cost of developing new features is $200,000, the technical debt ratio is 100,000 / 200,000 = 0.5. Here are some underlying cost associated with cost of fixing technical debt:- Cost of rework – The cost of fixing any bugs or issues in the existing codebase. This may include fixing coding errors, enhancing security measures, and improving performance.
- Cost of lost opportunity – This metric measures the potential revenue that could have been generated if the technical debt had not been present. For example, a delay in the product launch due to technical debt could lead to a loss of potential customers.
- Cost of maintenance – The cost of maintaining the existing technical debt over time, including fixing bugs and updating the codebase to keep up with changing technology.
- Cost of training – The cost of training new developers on a codebase that is difficult to understand due to technical debt.
- Cost of recruiting – The cost of attracting and hiring new developers to work on a codebase that is difficult to maintain due to technical debt.
- User Satisfaction
Feedback from end users on the quality and performance of systems, including satisfaction with speed, reliability, and functionality. This can be measured through user surveys, Net Promoter Score (NPS), or other methods. For example, if you survey 100 users and receive an NPS of 50, this indicates that 50% of users are likely to recommend your product to others. - Security Metrics
Metrics around security, such as the number of vulnerabilities, the frequency of security breaches, and the cost of security incidents. For example, if you have 10 vulnerabilities in your code and 4 of them are exploited, the exploitation rate is 4/10 = 40%.
I will write another post next week on what are some of the practices we can follow to manage the tech debt.
