Many organizations, including those developing software with Java from scratch, often prioritize speed to release a competitive version with adequate functionality. This urgency frequently leads to technical compromises in favor of delivering working code.
A great reason is often the pressure of timing: deadlines that are too close, requirements that change almost on a whim, or too much speed with which the software is expected to be developed. As a result, these speed-oriented development practices followed in this early phase are things that create quite a bit of technical debt, which would require even more time and effort to recover it all later.
In this article, we will overview the concept of technical debt, discuss preventive practices that help reduce accumulating technical issues, and provide some strategies for gradually eliminating them, with a particular focus on specific aspects.
What is technical debt?
Technical debt is a set of accumulated bugs, improper fixes, and suboptimal solutions that build up over time during development. It typically emerges when businesses opt for speed over quality. Examples of this include quick releases to beat competitors, taking shortcuts along the way, and rushing through testing.
The term of technical debt was introduced by Ward Cunningham, a programmer and co-author of the Agile Manifesto, in 1992. Cunningham compared technical debt to financial debt. In the financial world, money might be borrowed and put to investing in growth. On the other hand, such investments would involve costs in the form of interest payments. When interest payments are ignored, any debt would compound and work against growth. In software development, it reveals in wasted time and, correspondingly, costs from additional investigations.
Different studies have reported that software development time wasted due to technical debt ranges from 23% up to 42%. In the study "Technical Debt: An Empirical Investigation of Its Harmfulness and Management Strategies in Industry" by Terese Besker, it was found that developers reported spending approximately 23% of their working time addressing issues arising from technical debt.
According to Function's research published in May 2024, monolithic architectures tend to feel stronger impact of such realities. The investigation reveals that 57% of organizations spend more than a quarter of their IT budgets on remediation of technical debts, compared to 49% for organizations using microservices architectures.
The key to mitigating technical debt lies in its management. In the same way as in financial situations, too much technical debt can stifle growth and productivity, which is why it must be tackled by businesses in a timely and strategic manner.
The initial practices that provoke technical debt in Java applications
Below, we explore the root causes of technical debt in Java applications and how they impact maintainability over time.
1. New feature development is more important than code maintenance
The chief development task, especially in startups and organizations that speedily deliver a product into the market, is the developing of new features. New capability launches create an unwritten statement:
Even though this approach tends to improve speed for the short term, it overlooks code health: more complicated and error-prone modifications in the future. This kind of programming usually results in big monolithic classes, excessive use of singletons, and non-modular design, which makes it more difficult refactoring in Java applications over time.
2. Absence of continuous code review and refactoring
Existing Java code may not be reviewed or refactored because of the rush to ship. Without a culture of continuous improvement, short-term workarounds eventually solidify into rigid, unmanageable code structures. This technical debt settles gradually, to the point where even minor adjustments can require extensive investigation. With Java applications especially, outdated design patterns, excessive boilerplate code, and vastly bloated hierarchies complicate anything further.
3. No dedicated time for resolving technical debt
With a sprint only for visible progress, any improvement to reduce the technical debt is sidelined as unimportant. Such debts pile from unused dependencies, poorly handled Spring configurations, and obsolete frameworks in Java projects. Team members hardly schedule time to upgrade dependencies, optimize or remove redundant code; thus, efforts and time it takes to maintain that application just increases.
4. Scaling without architectural considerations
Many Java teams begin with simple structures: monolithic applications using Spring Boot and quick deployment. Those early things will then start to constrict flexibility and performance as the product scales. Without proactive architectural evolution (it may be modernizing from monolith to microservices), introducing a caching layer, or optimizing database queries, a Java application within three-five years gets slow and very difficult to scale.
The long-term impact: A ticking time bomb of tech dept
Over time, these negative practices we listed before may lead to the following consequences:
- Your team will have to spend more time on development: Everything changed in the software will have its impact in the future and require untying from the past decisions.
- You will have higher bug counts: An unrevised Java code becomes “fragile” and with each simple change brings in unintended side effects.
- New developers will get problems in onboarding: Newcomers will find it particularly hard to understand inconsistencies in code structures, thereby further prolonging ramp-up time.
- Your development method will no longer be agile: Rapid development begins to be slow due to the complexities brought about by the poor structure of the main system, which makes software maintenance itself difficult.
Summarizing the list, as the debts increase, so do the challenges for development teams, who have to maintain, upgrade, and scale their Java applications. A time-saving shortcut that was once considered a good idea can later turn into a bottleneck that impedes further innovation and increases operational expenses.
Strategies for managing and preventing accumulated technical debt in Java
Prevention and mitigation of technical debts in Java applications is possible with a proactive approach and sustainability in engineering practices. Below, we provide some key approaches in managing and reducing their life-long impact:
1. Allocate sprint time for technical debt reduction:
Instead of continually pushing it back, set aside time in each sprint to deal with technical debt. Keep a refactoring task backlog and devote a fixed percentage of development time–say 10 to 20%–to resolve these tasks before they blow into big issues. Examples of such would include eliminating redundant Java classes, optimizing Hibernate queries, and updating old dependencies.
2. Motivate through peer code reviews and pair programming:
Regular peer reviews can reveal problems early enough, before the amount of technical debt grows. Java applications, however, tend to be afflicted by long class hierarchies, bad dependency management, and inefficient exception handling. Code reviews will encourage best practices and compliance to Java coding standards (like the Effective Java principles), as well as knowledge-sharing across the project team.
3. Incorporate automated testing and continuous integration (CI) into your system:
You can use very popular testing frameworks, like JUnit, TestNG, and Mockito, to set up very structured and powerful tests for Java applications. A well-structured test suite will catch early regressions or unintended consequences that will greatly reduce future maintenance overhead. CI pipelines using tools like Jenkins, GitHub Actions, or GitLab CI/CD, where tests happen with every code commit, prevent unnoticed technical debt in the codebase.
4. Refactor code step-by-step rather than move for a complete overhaul:
It is dangerous, expensive, and very time-consuming to do massive code rewrites in Java. It is best to refactor the code incrementally, continually improving it with little steps to realize desired changes. Extract reusable components and deprecated APIs, and apply SOLID principles for easy and gradual transitioning toward cleaner code.
View our full checklist for software modernization
5. Adopt modular and scalable architecture principles:
Java applications should favor modular design for ease of maintenance. Hexagonal Architecture, Domain-Driven Design (DDD), microservices, and other architectural principles ensure loose coupling among components. Platforms like Spring Boot, Quarkus, and Micronaut further encourage modular designs with performance. Large monolithic services should be avoided in preference for RESTful APIs or event-driven architectures that scale and do not pile up technical debt.
6. Codebase changes and architectural decisions should be documented:
Java teams should be able to clearly document those architectural decisions, dependencies, and key system components. Using Javadoc, APA (Architecture Decision Records), and tools like Swagger/OpenAPI for API documentation allows future developers to have a quick understanding of the system. Having good documentation prevents knowledge silos and encourages responsible refactoring of the code.
7. Create a culture of awareness with respect to technical debt between stakeholders:
Technical debt in a Java application is not only an exposure to the developer; in fact, it affects the product manager, as well as the top management and the customer. Go on communicating about the risks that are associated with unchecked technical debt from time to time, and argue for an option where there is a balance of business priorities and long-term system health. Help stakeholders understand how technical debt has slowed down development through concrete examples of situations in order to help them get buy-in for proactive maintenance activities.
8. Track and evaluate technical debt through crucial metrics:
Use metrics such as code complexity, maintainability index, test coverage, and cyclomatic complexity to have a view over the health of the Java codebase. Use tools like SonarQube, PMD, Checkstyle, and FindBugs (now SpotBugs) to quantify technical debt, prioritize refactoring activities based on objective data and not based on pure assumptions.
Implementing a tech debt repayment plan
With a prioritization of the technical debt issues, you can design a strategy to repay them. Your aim is to give tech debt some space in your product development roadmap, together with new feature development, so as to limit high-interest rates.
Some teams dedicate time in every sprint to repay technical debt. For instance, 20% of user stories go toward code refactoring and fixing other problems, and 80% toward new feature developments. This is a good way to address some specific types of code defects and some production-level problems.
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Considering the level of complexity for tasks such as re-platforming, it might need to dedicate entire sprints just for solving this particular problem. In his swarm strategy, Simon Guest has suggested an annual swarm strategy whereby 20% of the contiguous sprints would be set aside at one time during the year for addressing technical debt. The strategy emphasizes collaborative problem-solving and rapid iteration. It is inspired by swarm intelligence, where decentralized and self-organized groups (similar to swarms in nature) work together to solve complex problems efficiently.
A different approach would be to have a dedicated team for the entire year to attack all aspects of tech debt repayment. If you find a scarcity of proper in-house resources for this, you could also consider engaging a vendor who would be able to offer both expertise in legacy system modernization and the right talents at various stages of the project.
The role of CI/CD and DevOps in dealing with technical debt
The fact that everything is manually built, with no automated tests, compounded with poor dependency management, thereby creates technical issues that tend to become inefficiencies in the course of time and slow down delivery.
In order to solve it, organizations have to gradually adopt build automation and introduce continuous integration and continuous delivery (CI/CD), which means automatic building, testing, and deploying code changes in a shared repository. Therefore,CI/CD facilitates software engineering work, reduces human error, and guarantees that such changes do not regress.
It is the adherence to DevOps principles that can prevent build and integration debts from accumulating. DevOps advocates for automated deployment, consistent environment configuration, and active monitoring—all of which remove inconsistencies and inefficiencies from the system over time. Here’s what you can as part of DevOps to manage technical dept:
- Review and optimize build scripts to prevent configurations from going out of date and causing compatibility issues.
- Use Infrastructure as Code (IaC) tools to ensure that infrastructure configurations are consistent and version-controlled.
- Incorporate static code analysis and automated code style checks into the CI pipeline to maintain high-quality, consistent code.
- Set up an effective version control system to keep an eye on changes and reduce risks during updates.
The organizations mature in DevOps and CI/CD practices are on the road to optimization and efficiency, reducing the weight of technical debt. A better implementing of CI/CD leads to an even more efficient build-and-deploy process, resulting in faster release cycles and more stable systems.
For example, TYMIQ provided DevOps-managed support for an industrial IoT (IIoT) platform. Thanks to TYMIQ's efforts, the client has achieved a better CI/CD implementation and a more straightforward release build. The team has simplified the microservices and merged functionalities within services. Additionally, TYMIQ supports junior developers by providing mentorship and ensuring that development processes align with best practices, preventing technical debt accumulation from the outset.
Conclusion
The need for fast time-to-market is vital in a lot of Java development environments, and it is here that code quality can be ignored in early phases, setting up long-term roadblocks that are much more expensive to resolve.
The key is not to eliminate technical debt just once, but to manage it wisely and continuously. By inserting sustainable coding practices in their early phases, Java teams can maintain speed while not jeopardizing future stability. At the end of the day, a good Java codebase is a fast-growing one that is also successful in the long run.
FAQ
1. What is technical debt in software development?
Technical debt is a set of accumulated bugs, improper fixes, and suboptimal solutions that build up over time during software development. It typically emerges when businesses opt for speed over quality. Examples of this include quick releases to beat competitors, taking shortcuts along the way, and rushing through testing.
2. What are some examples of technical debt?
The technical debt can be classified into three clearly stated types, depending on the cause of the debt. The following are some examples of each type:
- Code-level technical debt: Hardcoded values, code duplication, large monolithic classes, excessive use of singletons, and poor exception handling.
- Architectural technical debt: Tightly coupled components, no modularization, outdated dependencies, and scalability bottlenecks.
- Process-related technical debt: Insufficient code reviews, absence of automated testing and continuous integration (CI), and poor documentation.
3. How to measure technical dept on a project?
Measurement of technical debt helps teams analyze its impact and give priority to a refactoring effort. Here are the key ways to measure and track it:
- Code quality metrics – Analyze cyclomatic complexity, maintainability index, and code duplication using tools like SonarQube.
- Code reviews and issue tracking – Use static analysis tools (PMD, Checkstyle, SpotBugs) and track bug density to pinpoint trouble areas.
- Test coverage – Measure unit test coverage and use mutation testing to check test effectiveness.
- Architectural and dependency analysis – Find tightly coupled components and obsolete dependencies that are adversely affecting maintainability.
- Technical debt ratio (TDR) – Remediation effort versus development cost in the quantification of the debt impact.
4. How can companies assess the impact of technical debt on their systems?
Companies can assess technical debt by analyzing its impact on development speed, system performance, and maintenance costs. Key evaluation methods include:
- Tracking development slowdowns – Measure how long it takes to implement new features compared to past development cycles.
- Monitoring bug frequency and severity – Frequent or critical bugs indicate that technical debt is affecting system stability.
- Evaluating maintenance costs – Assess time and resources spent on fixing legacy code and refactoring.
- Measuring code complexity and maintainability – Use tools like SonarQube, PMD, and Checkstyle to analyze code duplication and maintainability index.
- Analyzing system performance – Identify slow response times and scalability issues caused by outdated code.
- Assessing developer productivity – Track how much time is spent on bug fixes versus feature development.
- Calculating technical debt ratio (TDR) – Estimate the effort required to fix issues relative to initial development time.
The assessment could be made by companies studying the effect technical debt has on their development velocity, performance of the system, and cost of training and maintenance. Some evaluation methodologies are as follows:
- Measuring development slowdowns – The time taken to implement features should be compared with times taken in past cycles of development.
- Monitoring frequency and severity of bugs – The occurrence of several trouble tickets or critical bugs could indicate that technical debt is hindering the system's stability.
- Evaluating maintenance costs – This method includes checking how much time and resources are spent on maintaining obsolete code and its refactoring.
- Measuring complexity and maintainability – There are tools like SonarQube, PMD, or Checkstyle that can help analyze the issues like code duplication and maintenance index.
- Analyzing system performance – Check for poor responsiveness and scalability issues triggered by outdated code.
- Assessing productive time – Measure the time spent on bug fixing versus feature development.
- Calculating technical debt ratio (TDR) – The effort to fix issues is estimated from initial development time.
5. How to avoid technical debt?
The most effective way to avoid technical dept is to prevent it from the beginning. Here are the core strategies that can help you achieve sustainable engineering, with no or minimal tech dept:
- Encourage peer code reviews and pair programming – Regular reviews help catch issues early, enforce best practices, and improve Java code quality.
- Track and measure technical debt – Use tools like SonarQube, PMD, and Checkstyle to quantify code complexity, maintainability, and refactoring priorities.
- Implement automated testing and CI/CD – Use frameworks like JUnit and tools like Jenkins to detect regressions early and maintain a stable codebase.
- Refactor incrementally – Avoid massive rewrites; instead, improve code step by step by extracting reusable components and applying SOLID principles.
- Adopt modular and scalable architectures – Favor microservices, Domain-Driven Design, and frameworks like Spring Boot to minimize technical debt.
- Document codebase changes and architectural decisions – Use Javadoc, API documentation tools like Swagger, and Architecture Decision Records to prevent knowledge silos.
- Raise awareness among stakeholders – Communicate the risks of technical debt to product managers and executives, showing how it slows down development.
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