Technical Debt Cost Calculator
Calculate maintenance costs based on industry research with proper academic disclaimers.
⚠️ What We're Actually Measuring
The Stripe 2018 study measures "toil, bad code, and maintenance" - not just technical debt. This includes all maintenance activities, not pure technical debt.
Use these numbers as benchmarks for total maintenance burden, not absolutes for technical debt specifically.
Number of developers on your team
Average developer salary including benefits
Results will appear here after calculating costs.
Domain Context (Synthesized from Research)
Rios et al. (2018) reviewed 362 primary studies. While they don't provide exact percentage bands, the studies suggest general patterns:
- Embedded systems: Generally lower technical debt burden (more constrained, tested systems)
- Web applications: Moderate technical debt burden (rapid iteration, varying quality practices)
- Financial services: Often higher technical debt burden (legacy systems, regulatory constraints)
Disclaimer: These are general patterns synthesized from multiple studies, not specific percentages from the paper.
Why accurate data beats inflated estimates
Understanding the difference between maintenance, toil, and pure technical debt—with real sources. For probabilistic project planning, see our Monte Carlo forecasting tool.
What Stripe actually measures
The 42% figure traces back to Stripe's 2018 Developer Coefficient report—the most recent edition they've released. It bundles "toil, bad code, and maintenance" into one maintenance burden metric, so it's broader than pure technical debt.
Academic context
Rios et al. (2018) reviewed 362 studies on technical debt. While they don't provide universal percentages, domain patterns emerge—embedded systems typically have lower TD burden than financial services with legacy constraints.
SQALE methodology
The SQALE method (Letouzey 2012) provides the remediation formula, but hour estimates come from tool implementations like SonarQube—not the academic paper. We clearly separate methodology from tool defaults.
đź“š All sources are properly cited with accurate context. No inflated percentages or misleading claims.
Frequently Asked Questions
Common questions about technical debt cost calculation and maintenance metrics.
Is technical debt the same as maintenance?
No, technical debt is a subset of maintenance work. Stripe's 2018 Developer Coefficient report measured "toil, bad code, and maintenance" as a combined 42% maintenance burden, which covers:
- Technical debt: Code quality issues that slow future development
- Toil: Repetitive, manual operational work
- Maintenance: All ongoing system upkeep activities
Pure technical debt costs are likely lower than the full 42% maintenance burden.
How accurate are SQALE remediation estimates?
SQALE estimates depend heavily on calibration to your specific codebase. The methodology from Letouzey (2012) is sound, but the default time values (8h for blockers, 4h for critical, etc.) come from SonarQube tool defaults, not academic research.
For accurate estimates, track actual remediation times for your team and adjust the multipliers accordingly. Different codebases, languages, and team experience levels will vary significantly.
What percentage of developer time should go to technical debt?
There's no universal "right" percentage—it depends on your domain, system maturity, and business context. The research shows general patterns:
- Embedded systems: Generally lower debt burden due to constraints
- Web applications: Moderate burden from rapid iteration
- Financial services: Higher burden from legacy system constraints
Focus on trends over time rather than absolute percentages. If your maintenance burden is increasing, investigate the root causes rather than targeting a specific number.
How do I measure technical debt in my codebase?
Effective technical debt measurement combines quantitative and qualitative approaches:
- Static analysis: Use tools like SonarQube, CodeClimate, or language-specific linters
- Cycle time tracking: Monitor how long features take from start to finish
- Developer surveys: Ask teams about friction points and maintenance burden
- Defect rates: Track bugs and rework in different parts of the codebase
Combine metrics with regular team retrospectives to identify the debt that actually impacts productivity, not just what automated tools flag.
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