Nine of ten Series-B diligence decks we’ve seen in 2024 asked the same three questions:
Below you’ll find:
A simple ROI worksheet that shows refactor cost vs. burn savings—used to green-light a $120 k cleanup that paid for itself in 3.5 sprints.
Tech-debt is not just messy code; it’s a capital efficiency drag.
VCs crunch debt metrics because:
Fact: Among 14 SaaS deals we supported in 2024, companies with debt ratio ≤ 25 % closed at a 23 % higher valuation multiple than peers.
Formula
java
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Debt Ratio = Remediation Cost ÷ Development CostToDate
bash
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sonar-scanner \
-Dsonar.projectKey=myapp \
-Dsonar.token=$TOKEN
curl -s \
“https://sonarcloud.io/api/measures/component?component=myapp&metricKeys=sqale_index” \
| jq ‘.component.measures[0].value’ # value in minutes
Convert minutes → dollars:
python
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rem_minutes = int(value)
rem_cost = rem_minutes / 60 * BLENDED_RATE # $65/h typical
Target:≤ 25 % before Series B.
Feature-tax = dev hours spent wrestling with legacy per story-point.
Formula
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Feature-Tax (hrs/SP) = Rework Hours ÷ Shipped Story-Points
Rework Hours – time spent on bug-fixes, hot-fixes, refactors inside the sprint.
python
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import requests, datetime, pandas as pd
JIRA=’https://my.atlassian.net’
JQL=f’sprint in openSprints() and labels = rework’
rework= requests.get(f'{JIRA}/rest/api/2/search?jql={JQL}’).json()
hours=sum(i[‘fields’][‘timespent’] for i in rework[‘issues’])/3600
sp= sum(i[‘fields’][‘customfield_10014’] for i in rework[‘issues’]) # SP field
tax= hours / sp if sp else 0
Benchmark Range
| Stage | Feature-Tax (hrs/SP) |
| Seed | 0.4 – 0.6 |
| Series-A | 0.8 – 1.2 |
| Series-B target | ≤ 1.0 |
Above 1.2 hrs/SP, investors assume an infra rebuild soon.
java
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Defect Escape = Prod Bugs / (Prod Bugs + QA Bugs)
Prod Bugs – tickets created from user reports or monitoring.
QA Bugs – found pre-production.
yaml
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expr: sum(increase(app_errors_total[1h])) BY (env)
for: 5m
labels:
severity: warn
annotations:
summary: “New prod errors spiking”
When spike pushes ratio > 0.2 for 24 h, defect-escape > 20 %—VC red flag.Series-B target:≤ 15 %.
python
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savings = (current_tax – target_tax) * sp_per_sprint * cost_per_hr
payback_sprints = refactor_cost / savings
Example
Add payback to investor memo → confidence boost.
| Panel | Source |
| Debt Ratio gauge | SonarCloud API |
| Feature-Tax trend | Jira REST |
| Defect-Escape pie | Grafana Loki |
| Burn multiple overlay | Sheet from Post #2 |
Dashboard URL shared read-only with investors; updates nightly.
Context – 1.1 M LOC Node/React monolith; aiming for $10 M Series B.
Before cleanup
Actions
After 2 months
| Metric | New | Change |
| Debt ratio | 24 % | –19 pp |
| Feature-tax | 0.9 hrs/SP | –0.7 |
| Defect-escape | 11 % | –12 pp |
VC diligence flagged “excellent engineering hygiene”; term-sheet signed 2 weeks later.
| Pitfall | Fix |
| Debt ratio drops but feature-tax doesn’t | Clean code but infra slow—optimize CI/job queues. |
| Sonar “won’t fix” noise | Create sonar.issue.ignore for generated code; keeps ratio honest. |
| Rework not logged | Add Git hook: PR label rework auto-applied if base branch hotfix/*. |
| Defect-escape looks great but users angry | Instrument Sentry; count silent exceptions as prod bugs. |
| Refactor freeze kills features | Parallel Flex squad builds new features while Core refactors—Micro-GCC advantage. |