The True Cost Of Technical Debt And Its Impact On Delivery

What is Technical Debt?

Technical debt refers to the implied cost of additional work caused by choosing an easy solution now instead of using a better approach that would take longer. Like financial debt, technical debt accrues interest payments in the form of extra effort and cost over time. Here are some examples of what causes technical debt:

Taking shortcuts in coding to hit a deadline
Using quick “duct-tape” solutions instead of robust, scalable architecture
Letting tests, documentation, and code maintenance lag behind feature development

As teams race to finish features and projects, these types of technical shortcuts pile up, making later changes and enhancements much harder. This delayed cost is “interest payments” teams “owe” down the road to address suboptimal code and technical cruft.

The Impact of Technical Debt

Like an unpaid credit card bill, technical debt in code, tests, or infrastructure grows over time and has ripple effects across teams:

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Increased cost to make changes: Code with high technical debt takes more time and effort to update as dependencies and side effects multiply.
More bugs and reliability issues: Fragile code prone to regressions drives up incident rates and impacts uptime.
Reduced developer productivity: Working in messy, hard-to-maintain code saps morale over time, slowing velocity as focus shifts to debt repayment.

In essence, the principal and interest costs of technical debt divert precious time and resources away from creating new customer value. And like credit card debt, it continues accruing rapidly if left unaddressed, making it seem impossible to catch up.

Quantifying Technical Debt

While easy to sense, technical debt is hard to measure precisely. Teams use various methods to quantify its size and cost:

Code quality analysis: Static analysis tools scan codebases to detect complexity, duplication, and style issues. Higher “principal” correlates with more future interest.
Defect density: The number of known bugs or incidents per lines of code signals reliability debt from lack of testing, monitoring, etc.
Velocity impact: Comparing output velocity between high- and low-debt code projects estimates loss from technical debt.

By assigning a notional cost to principal and interest payments over time, we can financially prioritize paying back different kinds of debt accrued.

Strategies to Reduce Technical Debt

Paying down technical debt is crucial for long-term velocity and efficiency. Teams use approaches like:

Code refactoring: Rewriting components to simplify structure, improve readability, remove duplication, increase robustness and scalability.
Adding tests: Expanding test coverage, shifting testing left, and embracing test-driven development to catch regressions.
Improving documentation: Keeping internal docs up-to-date ensures knowledge isn’t lost as team members change.
Tech debt sprints: Setting aside dedicated sprints to fix debt sends a clear priority signal.

To prevent new debt accrual, product leaders also need to align incentives around code quality versus just output velocity.

Case Studies on Tech Debt Management

Company X: Tackling Legacy Code

Online retailer Company X relied on aging PHP monolith powering the core shopping cart. Over 10+ years of quick fixes, the code had become extremely rigid. Developer velocity slowed to a crawl as even tiny changes caused regressions.

Company X launched a 18-month initiative refactoring core functions into decoupled Python microservices. By segmenting domains and applying test automation, velocity increased 4X after launch. Other benefits included easier third-party integration and higher infrastructure scalability.

Company Y: Adopting Modern Frameworks

Social app Company Y used outdated frameworks straining under rapid user growth. Page load times crept above 8 seconds, hurting engagement and signups. Developers struggled with tangled dependency chains making fixes arduous and risky.

The team chose to incrementally pay back debt by porting pages as React components inside the legacy architecture. After 6 months, perceived latency dropped under 1 second. Company Y could then rebuild other areas with React as a base, keeping velocity high.

Key Takeaways and Best Practices

Here are crucial lessons on minimizing the drag from technical debt:

Manage proactively: Regularly assess principal and interest costs to catch debt before it balloons.
Allocate fix time: Dedicate sprints to target debt backlogs instead of just accumulating more.
Prevent accrual: Coach teams on technical excellence and refactoring as part of normal work.
Use automation: Tests, linting, and other tools boost quality and provide debt visibility.

With vigilant tracking and priorities, teams can keep technical debt manageable. The key is setting aside time to deliberately pay it back by strengthening software foundations.

Example Code Snippets

Here is some sample code showing debt removal through refactoring.

Before

  function processOrder(order) {

    // lots of duplicative checks
    
    if (!order) {
      throw "No order provided"; 
    }
    
    if (!order.customer) {
      throw "Order missing customer";
    }
    
    // address checks
    
    // payment checks
    
    // inventory checks
    
    // shipping method checks
    
    // discounts checks

    // taxes logic

  }

After


  function isValidOrder(order) {
    // extracted checks
  }

  function calculateTaxes(order) {
     // extracted logic
  }

  function processOrder(order) {
    
    if (!isValidOrder(order)) {
      throw "Invalid order";
    }
   
    // cleaner logic flow

  }

The refactored version contains less duplication through extraction of order validation and taxes calculations. It will be easier to update, test, and reuse.