How to Measure Technical Debt: Metrics, Methods, and Ratios
Measuring Technical Debt: Metrics, Methods, and Ratios
- Technical debt is quantifiable; combine code quality, backlog analysis, and behavioral data for real clarity.
- Debt ratio frameworks make tradeoffs visible for leadership and engineers.
- Review your approach with both human insight and integrated automation for best results.
Technical debt slows teams, but generic audits miss what is actionable. Without measurement, debt stays a talking point instead of a decision driver.
Why measuring technical debt is urgent in 2025
Generative tooling introduces dramatic throughput gains, but it also multiplies the places where shortcuts creep into code, infrastructure, and runbooks. Boards and audit partners are already asking how teams will keep risk in check, and the answer has to be a metric that feels as rigorous as any operational KPI.
If you need a deeper historical walkthrough, combine this guide with the 2025 technical debt measurement playbook for context on governance rituals that keep debt conversations grounded.
Ward Cunningham's technical debt framing, documented by Martin Fowler, still resonates because it balances intentional shortcuts with the need to repay them. The moment you attach a credible metric to that story, engineering leaders gain the authority to request remediation time without sounding speculative.
Practical Ways to Measure Technical Debt
Quantitative Metrics: The Debt Ratio
The technical debt ratio equals Remediation Cost ÷ Development Cost. Estimate the remediation side by scoping backlog items, code review findings, and architecture upgrades that are required to restore a healthy system.
Use automated tools, PR review data, backlog analysis, and refactor projects to create realistic remediation cost ranges instead of gut feelings.
Development cost should mirror true engineering investment, whether you track sprints, story points, people hours, or direct spend.
Most organizations aim for a ratio under 0.2. Anything higher signals that liability growth is competing with shipping capacity.
Qualitative Signals: Review, Risk, and Reality
Run code reviews focused on maintainability, architectural clarity, and pattern adherence, not just syntax or lint errors.
Identify bottleneck areas where change is slow, buggy, or requires repeated workarounds, because those hotspots inflate remediation cost.
Map friction with both human feedback and CI pipeline signals so you know whether the pain is rooted in process, tooling, or design.
Continuous Debt Measurement for Modern Teams
Integrate measurement into every cycle so the ratio becomes a recurring part of sprint reviews and release planning.
Bring measurement data into planning and blueprint rituals. Use the automation ideas in our How to Solve AI Tech Debt guide to tie remediation scoring to real-time policy checks.
Executives trust ratios because they speak the same language as capacity planning. Treat the technical debt ratio as your shared language for prioritization, not an occasional slide.
Know what feeds the ratio
Ratios are only credible when the underlying numbers are sourced from the same rigor you use for feature estimates. Spend time preparing the data and documenting assumptions so the resulting KPI stands up to audits.
Inputs to capture before running the math
- Static analysis plus AI review insights. Consolidate linting, testing, and reviewer feedback inside Propel Code so every remediation point includes evidence.
- Backlog and roadmap context. Tie each hotspot to a clear remediation plan, as outlined in the technical debt measurement playbook, so stakeholders know what investments unlock progress.
- Delivery behavior. Compare the ratio against DORA metrics, incidents, and customer support themes to prove that resolving debt improves outcomes.
How to socialize the ratio
Present the ratio alongside the scenarios leadership cares about: effect on cycle time, audit posture, and roadmap promises. Emphasize that the goal is to steer investments, not to punish teams that inherited legacy systems.
Debt Ratio vs Other Metrics
Supporting Metrics
- Code Quality Index: great for spotting stylistic or hygiene issues but blind to architectural scale problems.
- Productivity impact metrics: reveal bottlenecks in delivery yet rarely express the cost to remediate root causes.
- Qualitative review insights: capture nuance but require facilitation and rarely roll up into executive dashboards.
Technical Debt Ratio
- Connects remediation cost to current engineering investment so cross-functional leaders see an apples-to-apples KPI.
- Easy to trend quarter over quarter once refactor candidates and their effort ranges sit in the backlog.
- Forces teams to scope and price refactors with the same rigor they apply to net-new features.
A repeatable playbook for calculating technical debt
Step-by-Step: Measuring Your Team's Technical Debt
Inventory the codebase
Identify legacy or high-risk areas with static analysis, dependency maps, and targeted engineer interviews.
Estimate remediation costs
Scopes fixes in effort hours or story points for each system you need to stabilize.
Calculate the development baseline
Sum total engineering effort for the last release, sprint, or milestone you want to use in the denominator.
Apply the technical debt ratio
Divide remediation cost by development cost and document the assumptions behind each input so you can trend it later.
Share results with leadership
Use the ratio as strategic input for planning and prioritization, not as a blame exercise for individual teams.
Frequently Asked Questions
Continuous visibility keeps debt from piling up
The ratio is only useful if it lives inside the rituals where priorities are set. Feed the data into sprint planning, QBR decks, and AI guardrails so every change either pays the debt down or at least avoids adding new liabilities. When you combine this approach with the automation ideas in our AI tech debt guide and productized checks inside Propel Code, your team can spot, price, and resolve technical debt long before it stalls delivery.
About the author: Tony Dong
Tony Dong is Propel's founder and CEO and previously served as VP of Engineering at Rippling, where he built operating reviews that balanced speed with governance. He has led engineering teams across startups and public companies, so he understands how debt signals surface in real delivery data.
Other articles such as the developer productivity metrics guide and the AI tech debt playbook extend this same system across planning, review, and remediation workflows.
Surface technical debt in every PR
Propel Code combines AI reviews, policy automation, and analytics so your team tracks technical debt where work happens.
