Code We Trust's Quality Standard
Code We Trust's Quality Standard transforms raw static analysis into business-grade intelligence — giving executives, investors, and engineering leaders a common language for code risk.
$1.52T
Estimated accumulated software technical debt in the U.S.
— CISQ, 2022
30–40%
Of IT budgets consumed by technical debt management
— Industry avg.
20%
Higher revenue growth in the top Tech Debt Score quintile vs. the bottom quintile
— McKinsey, 2022
6×
More expensive to fix a bug post-deployment vs. during design
— NIST
Traditional code audit tools produce hyper-detailed reports for senior developers — leaving executives, investors, and M&A buyers unable to interpret risk.
"Technical debt is like dark matter in technology — unseen but with significant impacts on business operations."
Old Paradigm
Code We Trust
Each dimension is independently measurable and consistently applicable across any codebase.
What It Is
Violations of best programming practices — logic errors, incorrect outputs, runtime failures.
Why It Matters
Bugs accumulate silently. Each unfixed defect increases future change costs exponentially.
Business Impact
Every bug in production costs 6× more to fix than one caught during design. Strongest predictor of maintenance cost.
The TDR is classified into six grades, inspired by the SQALE method.
Stable, predictable development. Innovation capacity fully available.
Minor issues. Manageable with regular maintenance cycles.
Refactoring recommended. Innovation capacity beginning to constrain.
Significant portion of team capacity consumed by maintenance.
Maintenance dominates development. End-of-life planning required.
Remediation cost exceeds rebuild cost. Retirement recommended.
CodeWeTrust assessed 20 widely-used open-source AI frameworks.
Frameworks with lower TDR are associated with faster innovation cycles.
CodeWeTrust's C2M platform applies a targeted 5-step audit pipeline.
Proven AST-based SAST, SCA, and SBOM tools systematically scan the codebase for all quality dimensions.
Full commit history analysis detects contributor risk, module volatility, and development patterns.
Identifies code areas combining high complexity + instability + criticality.
LLM reasoning applied selectively over hotspots. AI answers business-facing questions at scale.
Results formatted for three audiences: devs, managers, and executives — in days, not weeks.
The Quality Standard applies across the full lifecycle of software.
Software quality characteristics and sub-characteristics. 2023.
Source ↗Automated source code quality measures. 2021.
Source ↗Open source security assurance (OpenChain). 2023.
Source ↗Technical debt classification and remediation cost estimation. 2012.
Source ↗Reckless vs. prudent, deliberate vs. inadvertent debt classification. 2009.
Source ↗Technical Debt Score correlation with revenue growth. 2022.
Source ↗Cost of Poor Software Quality in the U.S. report. 2022.
Source ↗Defect resolution cost distribution across SDLC phases. 2002.
Source ↗Upload your metadata file and see how your codebase compares against industry benchmarks.