Software Engineering Articles

An emerging perspective in modern software development, influenced by lean methodology and from works like The Goal, Lean Startup, and Project to Product, is that mistakes and experimentation are essential for learning. This often means releasing imperfect software into production, which naturally creates some technical debt. The initial shortcuts or compromises are the principal, invisible to users but clear to developers, while the long-term impact bugs, quality issues, and slower delivery is the interest. The key distinction is between deliberate, prudent debt incurred for speed and learning, versus reckless debt caused by carelessness. Rather than striving for perfection or rewarding sheer volume of code, successful teams focus on delivering incremental units of value, accepting manageable debt as part of an adaptive and iterative software process.

For example, in a major banking initiative that was built on MongoDB, Kafka, AWS, and the Spring Framework technology stack and related java-based stack, technical debt accumulated rapidly due to shortcuts taken by the offshore vendor team under tight delivery timelines. Instead of carefully planning data models and adhering to MongoDB best practices, collections were loosely structured, queries became inefficient, document exceeding the limit supported and schema inconsistencies began to appear across services. Unit testing was often gamed or skipped to meet deadlines, leaving brittle codebase with hidden defects. Kafka was introduced for event streaming, but without proper design standards or validation pipelines, issues like message duplication, too many events that were not needed and processing delays surfaced. Over time, these gaps created mounting operational inefficiencies and raised long-term maintenance costs.

Although an on-site technology team provided governance, the distributed offshore model made reviews largely reactive rather than preventative. By the time design flaws were identified, many had already been deployed into production, making remediation costly and disruptive. This resulted in mounting technical debt that surfaced as constant rework, frequent patching, and a noticeable decline in delivery velocity. Beyond the technical inefficiencies, the absence of consistent standards and robust quality controls posed risks to regulatory compliance and eroded customer confidence two non-negotiable priorities in the banking sector. Ultimately, this case illustrates how unmanaged technical debt in mission-critical financial systems can quietly erode both business agility and long-term system resilience.

So technical debt is the implied cost of choosing a quick or easy solution today instead of a better, more sustainable one that might take longer to implement. Just like financial debt, it allows teams to move faster in the short term but creates a repayment burden later in the form of rework, reduced productivity, lack of flexibility for further extension and increased system fragility. It often arises from poor design, lack of testing, rushed development, or skipping best practices, and while some debt can be intentional and manageable, un-managed technical debt accumulates and can slow down innovation, increase risks, increase costs and make systems harder to maintain over time.

Technical debt is often categorized by its origin and the awareness among the team as when it was incurred during the development life cycle. I will write about these later on. There are categories as how we classify debt and some are

• Good Debt vs. Bad Debt : –
  • Good Debt : Debt taken on knowingly and strategically to achieve a clear, immediate business goal (e.g., shipping a feature quickly to beat a competitor). The team accepts the risk and plans to pay it back.
• Deliberate vs. Accidental : –
  • Deliberate Debt : The team decides to take the shortcut (e.g., hard coding a value) to meet a deadline. This aligns with prudent debt.
  • Accidental Debt (or Unintentional): Debt that accumulates over time due to evolving understanding of the product, new business requirements, or learning that a previous design decision was simply incorrect. This is often the largest source of debt.

Technical debt can be classified as

• Process-Related Causes
  • Rushed development to meet tight deadlines.
  • Frequent scope or requirement changes without redesign.
  • Short-term fixes and workarounds prioritized over long-term solutions.
  • Lack of regular code reviews or quality assurance checkpoints.
  • Inadequate planning for scalability and maintainability.
• People-Related Causes
  • Limited technical expertise or lack of training in tools/frameworks.
  • Poor communication between business and technical teams.
  • Misaligned priorities between stakeholders (e.g., speed vs. quality).
  • Inconsistent coding practices across distributed or offshore teams.
  • High turnover, leading to knowledge gaps and loss of context.
• Technology-Related Causes
  • Incomplete or poor data modeling and architecture.
  • Skipping unit tests, integration tests, or automated testing.
  • Not following best practices for databases, frameworks, or cloud services.
  • Overly complex, bloated, or redundant code base.
  • Legacy system dependencies without modernization planning.
  • Insufficient or outdated documentation.

Some of the business domain applications where I have seen very high technical debt are in

• Banking and Financial Services
  • Applications related to Core banking systems, payment processing, credit risk engines.
  • Many banks rely on decades-old COBOL-based mainframe program integrated with newer systems (e.g., API’s, mobile apps). Rushed compliance updates, fragmented data models, and vendor-driven offshore development often leave behind fragile architectures.
• Healthcare and Life Sciences
  • Applications related to Electronic Health Records (EHR), patient portals, insurance claims processing.
  • Systems are typically a patchwork of legacy software tied together with new cloud or AI modules. Strict compliance (HIPAA, GDPR) leads to quick-fix security patches, while poor interoperability standards create messy integrations across hospitals, labs, and insurers. Offshore Vendor Driven Development often leads to Technical Debt due to various reasons like gaps in skills, requirements misunderstanding etc.
• Telecommunications
  • Billing systems, customer management platforms, network monitoring.
  • High user volumes force companies to add features quickly. Mergers and acquisitions introduce multiple legacy stacks, leading to duplicated logic and fragile middle-ware layers. Billing engines especially carry massive customization with poor documentation. Offshore Vendor Driven Development often leads to Technical Debt due to various reasons like gaps in skills, requirements misunderstanding etc.
• Retail and E-Commerce
  • Inventory management, omnichannel order fulfillment, personalization engines.
  • Fast-moving competition drives teams to push out features without long-term design. Legacy ERP systems often fail to scale with cloud-based microservices, creating complex, high-maintenance integrations.

Key Strategies that help to deal with Technical Debt are

• Identify and Track Debt : – Maintain a “technical debt register” or backlog itemizing known issues.
• Prioritize by Impact :- Tackle the debt that most affect business outcomes (e.g., security risks, customer experience).
• Refactor Incrementally :-  Improve code, data models, or tests in small steps rather than waiting for big rewrites.
• Adopt Testing & Automation :-  Use unit, integration, and regression testing with CI/CD pipelines to prevent new debt.
• Set Standards & Best Practices :-  Enforce coding guidelines, architecture reviews, and documentation practices.
• Communicate in Business Terms :-  Explain the cost of debt as slower delivery, higher risk, or lost revenue to gain stakeholder buy-in.

Dealing with technical debt is less about eliminating it entirely and more about managing it strategically. Teams must acknowledge that some debt is intentional taken on to move quickly and should plan to repay it before it accumulates interest. By embedding refactoring into regular sprints, strengthening automated testing, and aligning teams on best practices, organizations can gradually reduce hidden risks while still delivering value. Importantly, leaders need to view technical debt not as a purely technical issue but as a business trade-off; when its impact is communicated in financial and customer terms, it becomes easier to secure time and resources for remediation.

The cost of resolving technical debt can be significant, often consuming 20–30% of a project’s budget depending on its severity and how long the debt has been left un-managed. For example, minor issues such as missing unit tests or small refactors may take days or weeks to resolve, costing a fraction of the sprint. In contrast, large-scale debt—such as poor data modeling, outdated frameworks, or legacy integrations—can extend timelines by several months and add millions of dollars in remediation costs for enterprise projects. The longer the debt remains, the more “interest” it accrues: bugs take longer to fix, new features take longer to deliver, and maintenance costs grow exponentially. Industry studies suggest that organizations often spend up to 30% of their development time addressing technical debt rather than delivering new features, making proactive debt management essential to avoid ballooning project costs and delays.

By solving technical debt, organizations gain both short-term efficiency and long-term resilience in their software systems. Reducing debt improves developer productivity, since clean, well-structured code base are easier to maintain, extend, and debug meaning less time wasted on workarounds and rework. It also strengthens system reliability and performance, as refactored architectures reduce bugs, downtime, and inefficiencies. From a business perspective, addressing technical debt lowers project costs by minimizing maintenance overhead, accelerates time-to-market for new features, and ensures smoother compliance with security and regulatory requirements. Just as importantly, it boosts team morale and collaboration, because developers spend more time innovating and less time fighting fragile code.

References : –

Sourcery. (2022, September 24). The impact of technical debt

Martini, A., Besker, T., & Bosch, J. (2018). Technical debt tracking: Current state of practice.