What Is Technical Debt?
Technical debt is the future cost of choosing a quick, easy solution now instead of a better approach that takes longer to implement during software development. Similar to financial debt, this “debt” accrues “interest” in the form of future rework, maintenance, and increased costs, which can slow down future progress if not addressed.
Technical debt is often incurred to achieve short-term gains like faster delivery but can become a significant problem if it accumulates to a point where it becomes difficult to make changes or adds excessive bugs.
Causes of technical debt include:
- Prioritizing speed over quality: Rushing to meet a deadline by taking shortcuts.
- Lack of awareness: Choosing a suboptimal solution without understanding its long-term implications.
- Poor documentation: Leaving future developers in the dark about how the system works.
Outdated code: Relying on old, unsupported, or inefficient code that requires constant
Types of Technical Debt
Architectural Debt
Architectural debt arises from design decisions that limit a system’s scalability, flexibility, or maintainability. It typically stems from an architecture chosen without enough foresight or understanding of future requirements. For example, monolithic systems might be faster to build at first but can impede modular updates or integration with other services as needs evolve.
Architectural debt often lurks beneath the surface and only becomes visible when the system faces significant changes, making it difficult and expensive to address. Reworking fundamental design choices can involve extensive code refactoring, retraining teams, or rebuilding systems from the ground up.
Code Debt
Code debt deals with poor coding practices, messy logic, or outdated structures within the codebase. Examples include duplicative code, lack of modularity, hardcoded values, or ignoring modern best practices. Developers might incur code debt when pressured to deliver features quickly or when working without established code standards.
This debt often causes future changes to take longer than they should, increases the likelihood of bugs, and frustrates onboarding of new developers. Addressing code debt generally involves refactoring code, introducing better patterns, and enforcing style consistency. While tackling isolated portions may be straightforward, unmanaged code debt accumulates and leads to “spaghetti code” and increases the risk of introducing defects during enhancements or bugfixes.
Infrastructure and DevOps Debt
Infrastructure and devops debt arises when temporary implementations, lack of automation, or outdated setups underpin the delivery pipeline and hosting environments. Rushed or neglected configuration management, manual deployment steps, and reliance on legacy servers contribute to this debt.
Over time, these decisions reduce operational efficiency, slow down releases, introduce security risks, and make it costly to adopt new technologies or scale infrastructure appropriately. This debt becomes especially problematic as applications grow and teams demand faster and more stable deployment cycles. Addressing it often requires investing in automation, configuration as code, and modern cloud-native practices.
Process Debt
Process debt reflects inefficiencies and gaps in development methodologies, team organization, or workflows. It can result from using outdated project management practices, failing to adapt processes as teams scale, or bypassing essential steps like code reviews or quality assurance cycles. This debt manifests as unclear responsibilities, duplicated work, missed deadlines, or inconsistent outcomes in software delivery.
Recovering from process debt often involves revisiting and updating workflows, introducing current methodologies like agile or devops, and ensuring continuous process refinement. Addressing process debt is not a one-off effort; it requires buy-in from across the organization and an ongoing cultural shift toward efficiency and collaboration.
Security Debt
Security debt accumulates when security measures are skipped, postponed, or inadequately implemented during software development. This can involve skipping penetration tests, deferring patching, using deprecated libraries, or lacking comprehensive security reviews. While such decisions might expedite delivery, they leave exploitable vulnerabilities that can put user data and system integrity at risk.
The consequences of unchecked security debt can range from data breaches and compliance violations to irreversible damage to a company’s reputation. Addressing security debt requires proactive investment in secure coding practices, regular vulnerability assessments, code audits, and dedicated security-focused sprints as part of the development cycle.
Related content: Read our guide to technical debt examples (coming soon)
Causes of Technical Debt
Prioritizing Speed over Quality
Organizations often face pressure to deliver features or products quickly to gain a competitive advantage or meet market demands. In these situations, teams may intentionally cut corners, skip testing cycles, or defer technical best practices to hit aggressive deadlines. This short-term gain creates long-term pain, as the skipped steps result in missed edge cases, higher maintenance costs, and more frequent production issues.
The trade-off becomes apparent when later changes are more difficult or risky due to these initial shortcuts. Technical debt formed in the name of speed eventually catches up, requiring more time and resources to resolve than would have been spent doing it right the first time. It is essential to balance time-to-market pressures with a disciplined commitment to quality to minimize this type of debt.
Lack of Awareness
Technical debt can accumulate simply because teams or stakeholders are unaware of its existence or the severity of its impact. When development teams don’t document shortcuts or fail to communicate technical compromises, decision-makers may assume the codebase is healthier than it is. This lack of transparency leads to missed opportunities for early intervention and allows debt to grow unchecked.
Cultivating technical debt awareness requires creating a culture where technical concerns are openly discussed and tracked. Regular reviews, open documentation, and visible dashboards help teams keep debt in check and ensure it is consistently prioritized alongside new features and bug fixes.
Poor Documentation
Insufficient or outdated documentation is a significant cause of technical debt. When code, architecture, and processes are not thoroughly documented, knowledge is lost each time a team member leaves or new developers join. This leads to repeated misunderstandings, reinventing existing solutions, and an overall reduction in development efficiency.
Addressing this cause involves making documentation a core part of the development workflow rather than an afterthought. Effective documentation ensures knowledge transfer, enables onboarding, and allows teams to identify and resolve technical debt with greater precision. Continuous updates and reviews are needed to keep documentation aligned with the evolving system.
Outdated Code
Outdated code results from neglecting to update libraries, frameworks, or language versions as technology advances. Over time, this causes a codebase to become incompatible with current tools or security practices, making it harder to find support, fix bugs, or integrate new features.
Regularly revisiting dependencies, upgrading frameworks, and refactoring out obsolete components are necessary to prevent outdated code from crippling a project. Without proactive modernization, the accumulated tech debt becomes more complex and expensive to resolve, increasing the risks of critical failures or security incidents.
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Tips from the Expert
In my experience, here are tips that can help you better manage technical debt as a strategic asset rather than a liability:
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Define and track a “tech debt interest rate” per component: Quantify how much each module or service slows down development due to its debt (e.g., extra hours per change). This exposes the most “expensive” debt and drives smarter remediation priorities based on cost-to-deliver, not just severity.
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Establish a product debt registry with business impact scoring: Maintain a shared backlog of debt items annotated with direct business risks like blocked features, compliance exposure, or SLA penalties. This reframes technical debt in business terms, encouraging executive support and alignment.
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Use debt ceilings to prevent runaway accumulation: Set maximum allowable thresholds for metrics like code complexity, test coverage, or dependency age. When breached, the team must stop and remediate before continuing new feature work, enforcing discipline before the system becomes unmanageable.
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Track root-cause linkage between incidents and known debt: Correlate bugs or outages to underlying debt items. This builds a historical record that quantifies debt impact and justifies investments in remediation by showing operational drag and reliability risk.
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Create per-team debt health reports for leadership visibility: Generate quarterly summaries that show each team’s debt load, trends, and mitigation progress. This increases transparency, encourages healthy competition, and elevates the visibility of systemic issues needing cross-team collaboration.
Consequences of Technical Debt
Accrued technical debt can have several severe consequences for organizations.
Reduced development speed
As technical debt accumulates, making changes to the codebase becomes more difficult and time-consuming. Developers spend more time understanding convoluted logic, avoiding side effects, and navigating inconsistent structures. This slows down feature delivery, increases development costs, and undermines responsiveness to business needs.
Increased defect rate
Codebases burdened by technical debt are more prone to bugs. Poor structure, unclear logic, and missing tests create an environment where even minor changes can introduce regressions. The quality of the software deteriorates over time, leading to more incidents in production and higher support costs.
Lower team morale
Working in a heavily indebted system is frustrating for developers. Frequent firefighting, brittle systems, and opaque code reduce job satisfaction and increase turnover. Teams may lose motivation when their efforts are consumed by patching legacy issues instead of building new value.
Higher maintenance costs
Technical debt leads to inefficiencies that compound over time. Tasks take longer, environments become harder to manage, and troubleshooting grows more complex. As a result, organizations spend more resources just to keep systems running, diverting effort from innovation or competitive improvements.
Barriers to innovation and scalability
Legacy architecture and outdated components limit the ability to adopt new technologies or scale applications efficiently. Integrating with modern tools or platforms often requires major overhauls, which are expensive and risky. Technical debt becomes a drag on innovation and a barrier to entering new markets or delivering new capabilities.
Security and compliance risks
Neglected systems often harbor unpatched vulnerabilities, outdated dependencies, and insufficient monitoring. These weaknesses create entry points for attackers and can result in serious compliance violations. The longer debt remains unaddressed, the greater the risk to data integrity, customer trust, and legal standing.
Best Practices for Controlling and Managing Technical Debt
Here are some of the ways that organizations can reduce and better manage their technical debt.
1. Leverage Dependency Mapping for Visibility
Dependency mapping reveals the relationships between various components, modules, and services within a software system. By visually identifying interconnections, teams can easily spot areas where technical debt may create bottlenecks or heighten failure risks. This transparency helps prioritize which dependencies require refactoring, replacement, or closer review due to accumulated debt.
Performing regular dependency audits and mapping sessions ensures that hidden complexity does not grow unchecked. Clear maps enable cross-team communication and foster a shared understanding of how technical debt is distributed. As a result, organizations are better equipped to target high-priority areas and avoid adding new debt when making changes.
2. Categorize Debt for Better Understanding
Not all technical debt is equal: some forms pose immediate risk, while others can be strategically deferred. Categorizing debt (such as distinguishing architectural, code, or process debt) enables engineering teams to contextualize the severity, causes, and potential impact of different types. This structured view supports better prioritization, targeted remediation, and clarity when communicating technical debt status to stakeholders.
By adopting a consistent classification framework, organizations can tie debt categories to specific metrics, remediation strategies, and ownership assignments. This targeted approach ensures that critical debt receives appropriate focus while less urgent debt is managed within the regular development lifecycle, preventing chaos from generalized or scattered efforts.
3. Integrate Automated Testing at Every Stage
Automated testing is essential in controlling technical debt because it provides immediate feedback on defects, regressions, or side effects of code changes. Continuous integration pipelines that incorporate unit, integration, and regression tests make it much easier to detect debt-related issues before they reach production. By catching problems early, teams reduce the effort required for remediation and prevent further accumulation of technical debt.
Implementing automated tests is a proactive investment that pays dividends over time. It supports code maintainability and quality, reduces manual verification costs, and builds confidence in making future changes. As technical debt is retired, the safety net provided by tests helps prevent its reintroduction.
4. Use Metrics and Dashboards to Continuously Track Debt
Quantifying technical debt with actionable metrics (such as code complexity, number of code smells, or dependency age) provides a basis for informed decision-making. Visual dashboards offer at-a-glance tracking, trend analysis, and visibility into both progress and areas of concern. These tools reinforce accountability and ensure that technical debt remains a visible priority, not an afterthought.
Regularly reviewing dashboards and incorporating debt reduction goals into sprint planning or OKRs (Objectives and Key Results) ensures continuous attention. This data-driven approach fosters an ongoing improvement culture and helps teams pivot quickly when new debt risks emerge.
5. Adopt Architectural Decision Records and Design Reviews
Documenting major design and architectural decisions through architectural decision records (ADRs) increases transparency and ensures the context behind choices is preserved over time. Creating a habit of peer-reviewed design sessions and mandatory ADRs prevents the repetition of past mistakes and helps detect early signs of architectural debt.
This discipline also supports onboarding by giving newcomers immediate insight into why the system evolved in certain ways. Robust design review and documentation processes help teams approach future changes with an informed perspective and minimize ad hoc, debt-creating decisions.
6. Prevent Last-Minute Scope and Schedule Changes
Last-minute changes to project requirements or delivery timelines often force teams to cut corners and defer essential technical work. Formalizing change management processes ensures that any late changes are properly evaluated for their potential to add technical debt. Including technical leads in decision-making helps balance business priorities with engineering realities.
Establishing buffer time in schedules and maintaining a “definition of done” that includes technical quality checks reduces the pressure to overlook debt considerations. Proactively managing scope and sticking to planned schedules leads to more sustainable codebases and less cumulative technical debt.
7. Apply AI-Assisted Code Analysis and Modernization Tools
Modern AI tools can automatically detect code smells, suggest refactoring opportunities, and generate tests based on changes. Incorporating AI-assisted analysis accelerates the identification of hidden or emerging technical debt, especially in large, complex codebases where manual review is not feasible.
Some tools also support automated modernization by identifying obsolete dependencies, recommending language upgrades, or refactoring entire components. Leveraging AI-driven platforms shortens debt remediation timeframes and provides objective insights that drive consistent best practices across teams.
Identifying and Managing Technical Debt with Faddom
Technical debt often increases when teams lack a clear understanding of their environment. Unidentified assets, undocumented dependencies, and legacy connections complicate the assessment of risk, planning for changes, and the safe removal of outdated components. Without accurate visibility, organizations tend to maintain complexity instead of reducing it.
Faddom fills this gap by continuously discovering real-time application dependencies and network traffic across hybrid environments. By clarifying which systems are connected, identifying actively used resources, and pinpointing areas of unnecessary complexity, teams can confidently simplify their architectures, eliminate unused or over-provisioned infrastructure, and make informed decisions during optimization and change initiatives. This enhanced visibility not only helps prevent the accumulation of new technical debt but also systematically reduces existing infrastructure and architectural debt.
To learn how dependency visibility can support the reduction of technical debt, fill out the form on the right to schedule a demo!
