I could say “What is technical debt and how to avoid it”, but that is less clickbaity 😀 Working on some open source projects now (like Compose), our aim to avoid inserting it is sometimes overtaking delivery velocity as well. Technical debt refers to the cost of maintaining and updating a codebase as it grows and evolves over time. It is often incurred when shortcuts are taken during development in order to meet deadlines or budget constraints. Some dreadful examples of technical debt include:
- Using hacky or poorly-designed code that is difficult to understand or modify
- Not properly commenting or documenting code
- Not following established coding conventions or best practices
- Not properly testing code
- Not refactoring code as it becomes more complex
To avoid technical debt, it is important to prioritize maintainability and readability in the codebase, as well as to properly plan and budget for ongoing maintenance and updates. Some fun strategies for avoiding technical debt include:
- Following established coding conventions and best practices
- Writing clear and well-commented code
- Implementing automated testing and continuous integration
- Regularly reviewing and refactoring code to ensure it remains maintainable and readable
- Prioritizing long-term goals over short-term gains
- Allowing enough time for code reviews, testing and documentation
- Building and following a clear process with defined milestones
- Building a culture of ownership and accountability of the codebase
By following these practices, organizations can minimize the amount of technical debt they incur, making their codebase easier to maintain and less expensive to update over time. So in my series of posts on Agile, it’s now the time to see, how these (debt and agile) are related. They are closely related, as both concepts deal with the maintenance and evolution of software over time. Agile development is a methodology that emphasizes flexibility, collaboration, and rapid iteration, with a focus on delivering working software as quickly as possible. Technical debt, on the other hand, refers to the cost of maintaining and updating that software over time. In an agile development environment, technical debt can be incurred when shortcuts are taken in order to meet deadlines or deliver features more quickly. For example, if a developer is under pressure to meet a sprint deadline, they might choose to use a hacky or poorly-designed solution that is quick to implement but difficult to maintain. This can lead to an accumulation of technical debt over time, as the codebase becomes more complex and harder to update.However, when agile development is done correctly and with the right mindset, it can help to minimize technical debt. Agile development methodologies prioritize continuous improvement, which helps to keep the codebase maintainable and readable. Agile also encourages regular reviews and refactoring of code, which can help to identify and address issues with technical debt.
In summary, Agile development and technical debt are closely related, as both deal with the ongoing maintenance and evolution of software. Agile development methodologies can help to minimize technical debt by encouraging continuous improvement, regular reviews and refactoring, but it can also be a source of technical debt if not done properly. Therefore, it is important to balance the speed of delivery with the long-term maintainability of the codebase.
Luckily, there are several ways to fight technical debt:
- Code review tools: These tools allow developers to review and approve code changes before they are merged into the codebase. Examples include Gerrit, Review Board, and Crucible, and of course the native tools of GitHub, GitLab, BitBucket and more.
- Linting tools: These tools check the codebase for adherence to coding conventions and best practices. Examples include Roslyn, Resharper, ESLint and Pylint.
- Refactoring tools: These tools assist developers in restructuring and reorganizing code to make it more maintainable. Examples include Resharper, Rascal and JDeodorant.
- Automated testing tools: These tools automatically run tests on the codebase, ensuring that changes do not break existing functionality. Examples include XUnit, JUnit, TestNG, and Selenium.
- Code coverage tools: These tools measure how much of the codebase is covered by automated tests, helping to identify areas where testing is insufficient. Examples include dotCover, Coverlet, Cobertura and Jacoco.
- Code quality tools: These tools analyze the codebase and provide feedback on areas that need improvement. Examples include NDepend, SonarQube, and CodeClimate.
- Dependency management tools: These tools help track the dependencies of a project and its sub-dependencies and ensure that the project is using the latest and most stable versions of the dependencies. Examples include Nuget, Maven, Gradle, and npm.
And this is the point I am going to break – some – people. Believe or not, sometimes technical debt can be useful too 😀 Mainly, it allows an organization to quickly deliver a working product or feature, even if it may have some long-term maintenance costs. Some situations where technical debt might be useful include:
- Rapid prototyping: When an organization needs to quickly create a prototype of a product or feature to test its feasibility, it may be acceptable to incur some technical debt in order to get the prototype up and running quickly.
- Meeting tight deadlines: When a project has a tight deadline and there is not enough time to properly plan and implement all the features, taking shortcuts can be useful to deliver a working product in time.
- Experimentation, Tryouts: Incurring some technical debt can be a good way to try out new technologies or features, as it allows an organization to quickly test the waters and see if they are viable before committing to a full-scale implementation.
- Short-term projects: In some cases, technical debt may be acceptable for short-term projects that will be phased out or replaced in the near future.
It is important to note that technical debt should not be taken lightly, as it can quickly accumulate and become a burden on the development process. Therefore, it should be used strategically, and with a clear plan to minimize and pay off the debt in the future. Furthermore, when incurring in technical debt, it is important to have a clear plan on how to address and pay off the debt in the future, it is also important to communicate to the stakeholders the risks of incurring in technical debt and the plan to pay it off.
So, to have a better idea of where you are on the fight with them, you should (yes, Agile!) create KPIs around them and measure them 🙂 Technical debt can be measured in a number of ways, including:
- Code review metrics: Code review metrics such as the number of comments, the number of issues raised, and the number of defects found can be used to measure the quality of the code and the ease of maintenance.
- Test coverage metrics: Metrics such as test coverage, that is the percentage of the codebase that is covered by automated tests, can be used to measure the robustness and maintainability of the codebase.
- Complexity metrics: Metrics such as cyclomatic complexity, which measures the complexity of a piece of code, can be used to identify areas of the codebase that are difficult to understand or maintain.
- Refactoring metrics: Metrics such as the number of refactoring performed, the number of code smells identified and fixed, can be used to measure the maintainability and readability of the codebase.
- Technical debt ratios: These ratios express the relationship between the cost of maintaining the system and the cost of developing it. An example is the debt-to-equity ratio, which compares the estimated cost of paying off technical debt to the estimated value of the system after the debt is paid off.
- User feedback: User feedback on the ease of use, the speed and the reliability of the system can be used to measure the impact of the technical debt on the end-user – nothing beats a good hallway testing.
It is worth noting that there is no one-size-fits-all approach to measuring technical debt, and it may be necessary to use a combination of different metrics to get a comprehensive understanding of the state of the codebase. Additionally, it is important to note that measuring technical debt is not a one-time process, but it should be done regularly, so that progress can be tracked, and improvements can be made over time.