In the fast-moving world of software development, technical debt is unavoidable. Like financial debt, it can accelerate growth when used intentionally. But if it is not monitored, it becomes a burden that slows delivery, increases risk, and weakens product quality.
In agile environments, where adaptability is essential, managing technical debt is a constant balancing act between delivery speed and long-term sustainability.
This article explores how to identify, quantify, and reduce technical debt in agile projects through practical strategies and field-tested best practices.
Identification of technical debt in agile projects
Understanding technical debt
Technical debt in agile software development refers to design and coding decisions that optimize for short-term delivery at the expense of long-term code quality. These decisions may look efficient now, but they often become expensive later.
Common causes
1) Tight deadlines and time pressure
Agile teams frequently operate under short delivery cycles. Under pressure, teams may compromise on testing, documentation, or clean architecture. These shortcuts accumulate and become technical debt.
2) Frequent requirement changes
Agile projects are designed to absorb change. However, repeated changes without architecture recalibration can create inconsistent code structures and ad hoc solutions that are harder to maintain.
3) Limited stakeholder and user involvement
When collaboration is weak, teams may build solutions that do not fully match user needs. This misalignment leads to major rework, repeated course corrections, and additional technical debt.
Understanding these causes helps teams reduce avoidable debt while preserving agility.
Signs and symptoms of technical debt
Code and design indicators
- Misused or inconsistently applied design patterns
- Duplicated code and weak naming conventions
- Poor project structure and readability
- Inconsistent architectural boundaries
A common microservices example is the API gateway pattern. Teams may define a gateway as the central entry point, but later bypass it through direct client or inter-service calls. This weakens consistency, security, observability, and maintainability.
Performance and maintenance indicators
Performance problems
- Inadequate design choices for real usage constraints
- Overloaded implementations for routine workflows
- Inefficient loops, poor database indexing, or memory misuse
- Legacy technical choices that are no longer fit for purpose (for example, using OLE Automation/COM document generation where OpenXML is a modern and safer option)
Maintenance difficulties
- “Temporary” fixes become permanent patches
- Every change feels risky and can break unexpected areas
- Debugging and updates become increasingly expensive
A useful analogy is Jenga: in fragile systems, changing one block can destabilize the entire structure.
Tools and techniques to identify technical debt
Peer code reviews
Code reviews detect debt early and improve knowledge sharing and coding consistency across the team.
Static code analysis
Automated tools help detect issues before runtime:
- SonarQube
- ESLint
- IDE static analysis tools (Visual Studio, Rider, and others)
Code quality metrics
Track indicators such as:
- Code complexity
- Test coverage
- Bug rates
- Maintainability trends over time
Contribution from testing professionals
QA specialists often uncover hidden defects, performance bottlenecks, and workflow inconsistencies that reveal deeper technical debt.
Quantifying technical debt
Methods to measure technical debt
Quantitative approaches
- Code complexity: High cyclomatic complexity often means higher maintenance cost.
- Load time and performance metrics: Degraded response times often signal structural inefficiencies.
- Bug volume and recurrence: Recurring defects in the same areas are strong debt indicators.
- Static codebase analysis: Duplication, code smells, and pattern misuse can be measured consistently.
Qualitative approaches
- Developer feedback on readability, modifiability, and friction points
- Stakeholder and end-user feedback on system limitations and reliability issues
- Code review insights on design consistency and long-term maintainability
Combining quantitative and qualitative perspectives gives a more accurate debt profile.
Project impact
Cost impact
Technical debt increases operational costs. Fragile systems require more time for analysis, modification, testing, and troubleshooting.
It also reduces innovation capacity: teams spend more effort fixing and less effort building new features.
Delivery impact
Technical debt causes delivery delays:
- Unexpected bug-fixing cycles
- Rework and code rewrites
- Longer debugging phases
- Dependencies blocked by unstable components
One problematic subsystem can create a domino effect across the roadmap.
Product quality impact
High technical debt can compromise reliability, performance, and security. Over time, this affects user satisfaction and business reputation.
A public example often discussed in this context is the TSB Bank migration incident in 2018, where major technology disruption had significant customer and financial consequences. See BBC coverage: https://www.bbc.com/news/business-64036529.
Best practices for managing technical debt in agile projects
After identifying and quantifying debt, the key is disciplined management.
- Prioritize and plan refactoring in regular delivery cycles.
- Maintain code review discipline and quality standards to prevent debt accumulation.
- Invest in automated testing to detect regressions early.
- Document code and architecture decisions for maintainability.
- Develop team skills continuously through training and knowledge sharing.
- Communicate transparently with stakeholders about technical debt and its impact.
- Run regular debt assessments to track progress and adjust strategy.
Technical debt is not a moral failure
Transparent communication must be collaborative, not accusatory. When teams feel safe discussing constraints and trade-offs, issues are surfaced earlier and managed more effectively.
A pragmatic approach to technical debt in agile teams
Technical debt management maturity usually evolves progressively with a team’s agile and engineering maturity:
- Stage 1: Reactive - Teams fix issues only when incidents occur
- Stage 2: Visible - Teams track debt and discuss it in planning
- Stage 3: Managed - Teams budget repayment and monitor risk trends
- Stage 4: Strategic - Teams link architecture investment to business outcomes
The objective is not zero debt. The objective is controlled debt aligned with product strategy.
Conclusion
Technical debt management is a core success factor in agile software delivery. It is a complex challenge, but also a strategic opportunity.
By understanding causes, recognizing symptoms, measuring impact, and applying pragmatic best practices, teams can balance agility with software quality and long-term maintainability.
The key is balance: fast delivery and adaptability on one side, durable architecture and engineering discipline on the other.
For organizations, this is not only an engineering concern. It is a leadership responsibility. Making technical debt visible, prioritized, and intentionally managed is central to sustainable software delivery and to the technical leadership approach promoted by Lilarisz.
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For a practical modernization example where architecture decisions and delivery discipline must stay aligned, explore the Legal ERP Modernization case study.
Original publication date: February 13, 2024. Originally published on LinkedIn: Gestion de la Dette Technique dans les projets agiles : stratégies et best practices.