How to Evaluate a Company's Technical Debt

Didier CAROFF

Apr 23, 2025 • 9 min read

Technical debt is often the hidden liability that can make or break an investment, acquisition, or strategic partnership. As an invisible cost looming in codebases and infrastructure, it requires methodical assessment to properly quantify risk and long-term impact. This guide offers a structured and thorough approach to evaluating technical debt during due diligence, helping stakeholders make informed decisions based on the complete technical landscape.

What Is Technical Debt?

Technical debt represents the future cost incurred when choosing expedient solutions over optimal ones. Like financial debt, it accrues "interest" over time—making systems increasingly difficult and expensive to maintain or enhance. Ward Cunningham, who coined the term, described it as "shipping first-time code that we will have to work on again later."

Technical debt manifests in multiple forms:

Deliberate debt: Conscious decisions to take shortcuts to meet deadlines
Inadvertent debt: Poor practices or limited understanding creating unintentional issues
Bit rot: Systems degrading over time through incremental, suboptimal changes
Architectural debt: Fundamental design flaws that limit system evolution
Knowledge debt: Loss of context and understanding of how systems work

Why Technical Debt Assessment Matters

Unaddressed technical debt can have far-reaching consequences:

Significantly increased maintenance costs as developers navigate complex, fragile systems
Dramatically slowed feature development and extended time-to-market
Reduced competitive advantage through inability to adapt quickly
Creation of security vulnerabilities and compliance risks
Limited scalability when facing increased user demand
Talent attrition as qualified engineers avoid working with problematic codebases
Increased operational costs from manual processes and frequent firefighting
Higher risk of catastrophic system failures
Difficulty integrating with modern tools and platforms

Preparing for Technical Debt Assessment

Before diving into assessment, establish the appropriate context:

Define Assessment Scope

Which systems and applications should be evaluated?
What aspects (code, infrastructure, processes) are most critical?
What level of detail is needed for decision-making?

Assemble the Right Team

Technical experts familiar with the technology stack
Domain specialists who understand the business context
Independent evaluators without emotional attachment to the codebase

Gather Documentation and Access

Source code repositories
Technical documentation
Deployment pipelines and infrastructure configurations
Production and monitoring systems
Development process documentation

Key Areas to Evaluate

1. Code Quality and Architecture

The foundation of any technical assessment begins with code quality. Analyze:

Static Code Analysis

Code complexity metrics: Examine cyclomatic complexity, cognitive complexity, and code duplication. Tools like SonarQube, CodeClimate, or CodeScene can provide objective measurements.
Code smells: Identify anti-patterns, excessive class sizes, overly complex methods, and poor abstraction.
Static code analysis results: Review existing static analysis reports or run tools to identify violations of coding standards.
Stylistic consistency: Evaluate adherence to coding conventions and consistency across the codebase.

Athat accurately represent the system existDesign patterns: Are appropriate design patterns used consistently?

Separation of concerns: Is the code properly modularized with clear responsibility boundaries?
Technical documentation: Are architectural decisions documented? Look for Architecture Decision Records (ADRs) or equivalent documentation.
Architecture diagrams: Do current diagrams that accurately represent the system exist?
Dependency management: How are internal and external dependencies managed and isolated?

Architectural Patterns

Monolithic vs. microservices: Is the architecture appropriate for the problem domain and team structure?
Scalability design: How does the architecture handle increased load?
Extensibility: How easily can the system incorporate new features or technologies?
Interoperability: How well does the system integrate with other systems?
Technical debt tracking: Is there a system for documenting and addressing known technical issues?

2. Testing Coverage and Quality

Insufficient testing represents significant technical debt and risk:

Test Coverage

Code coverage: What percentage of the codebase is covered by automated tests?
Coverage distribution: Is coverage focused on critical components or merely easy-to-test code?
Edge case coverage: Are boundary conditions and exceptional paths tested?
Regression coverage: How effectively do tests catch regressions?

Test Types and Balance

Test pyramid balance: Is there a healthy distribution of unit, integration, and end-to-end tests?
Unit tests: Do they test isolated components without external dependencies?
Integration tests: Do they verify interactions between components?
End-to-end tests: Do they validate complete user journeys?
Performance tests: Is the system's performance regularly tested against benchmarks?
Security tests: Are security vulnerabilities regularly tested?

Test Quality

Test isolation: Are tests independent of each other?
Test determinism: Do tests produce consistent results regardless of environment or execution order?
Test readability: Are tests easy to understand and maintain?
Test brittleness: Do tests break easily with minor changes?
Mock usage: Are external dependencies appropriately mocked or stubbed?

Testing Processes

Test automation: How integrated are tests in the development workflow?
Continuous integration: Are tests executed automatically on code changes?
Test-driven development: Is TDD or a similar methodology practiced?
Test environment parity: How closely do test environments match production?
Test documentation: How well are test cases and scenarios documented?

3. Infrastructure and Operations

Modern infrastructure practices significantly impact maintainability and scalability:

Infrastructure Management

Infrastructure as code: Are infrastructure configurations version-controlled and reproducible?
Environment parity: How similar are development, testing, and production environments?
Configuration management: How are application and environment configurations managed?
Secret management: How are credentials and sensitive information handled?
Infrastructure documentation: How well are infrastructure components and relationships documented?

Deployment and Release

Deployment automation: How automated and reliable are deployments?
Deployment frequency: How often are changes deployed to production?
Deployment safety: What safeguards exist to prevent and detect deployment issues?
Rollback capabilities: How quickly and reliably can problematic deployments be reversed?
Feature flagging: Is there capability to gradually release features or disable them if issues arise?

Observability and Support

Monitoring coverage: Are all critical components and services monitored?
Alerting quality: Are alerts actionable and targeted to appropriate responders?
Observability tools: What tools are used for logging, metrics, and tracing?
Log quality: Are logs structured, searchable, and appropriately detailed?
Incident response: How are production incidents handled and documented?
Post-mortem process: How are incidents analyzed to prevent recurrence?

Reliability Engineering

SLAs and SLOs: Are service-level objectives defined and measured?
Disaster recovery: Are backup and recovery processes documented and tested?
High availability design: How does the system handle component failures?
Chaos engineering: Is system resilience proactively tested?
Capacity planning: How are future resource needs predicted and addressed?

4. Technology Stack

The technology choices made historically and currently can create immediate or future debt:

Technology Currency

Language versions: Are programming languages , and supported?
Framework versions: Are frameworks and libraries up-to-date?
Operating systems: Are OS versions current and receiving security updates?
Database systems: Are database technologies modern and well-supported?
End-of-life technologies: Are any components approaching vendor end-of-support?

Dependency Management

Dependency freshness: How up-to-date are third-party libraries and frameworks?
Security patching: What is the process for addressing security vulnerabilities in dependencies?
Dependency bloat: Is the application carrying unnecessary dependencies?
Licensing compliance: Are all dependencies legally compliant with licensing requirements?
Supply chain security: How are dependencies vetted for quality and security?

Technology Strategy

Technology diversity: Is there unnecessary proliferation of languages and frameworks?
Build vs. buy decisions: Is the right balance struck between custom development and third-party solutions?
Technical governance: Are technology decisions made systematically or ad hoc?
Cloud strategy: Is cloud usage optimized for cost and capabilities?
Vendor lock-in: How dependent is the system on specific vendors or technologies?

Emerging Technology Adoption

Innovation process: How are new technologies evaluated and adopted?
Technical radar: Is there a structured approach to tracking relevant technologies?
Migration strategies: How are legacy technologies systematically replaced?
Experimentation culture: Is controlled experimentation with new technologies encouraged?
Skills development: How is the team's expertise with new technologies developed?

5. Development Processes and Team Practices

Process debt often accompanies code debt and can be equally impactful:

Development Workflow

Version control practices: How effectively is source code managed?
Branching strategy: What branching model is used, and is it appropriate?
Code review processes: Are changes peer-reviewed before deployment?
Pull request quality: Are PRs appropriately sized and documented?
Continuous integration: How automated is the integration and verification process?

Team Practices

Development standards: Are there documented and followed coding standards?
Knowledge sharing: How effectively is technical knowledge disseminated?
Knowledge silos: Is critical knowledge concentrated among few individuals?
Onboarding process: How quickly can new team members become productive?
Technical documentation: How well are systems and processes documented?

Development Environment

Developer tooling: How effective are the tools provided to developers?
Local environment setup: How easily can developers set up their environments?
Development-production parity: How closely does the development environment match production?
Build performance: How long do builds take, and how reliable are they?
Development feedback loops: How quickly do developers get feedback on their changes?

Agile and Engineering Practices

Refactoring discipline: Is refactoring routinely performed to improve code quality?
Technical debt prioritization: How is addressing technical debt balanced with new features?
Pair programming: Is collaborative development practiced?
Code ownership model: Is code collectively owned or siloed?
Engineering metrics: What metrics are used to assess engineering effectiveness?

6. Security and Compliance

Security debt can be the most costly form of technical debt:

Security Practices

Security testing: Is security testing integrated into development?
Vulnerability management: How are security vulnerabilities identified and addressed?
Authentication and authorization: How robust are identity and access controls?
Encryption practices: How is data protected in transit and at rest?
Security monitoring: How are security events detected and handled?

Compliance Status

Regulatory requirements: Does the software meet applicable regulatory requirements?
Industry standards: Does the system adhere to relevant industry standards?
Audit trail: Is system activity properly logged for compliance purposes?
Data governance: How is sensitive data managed and protected?
Compliance documentation: Are compliance-related procedures documented?

Quantifying Technical Debt

Technical debt must be translated into business impact to be meaningful to decision-makers:

1. Development Velocity Analysis

Historical productivity metrics can reveal the impact of technical debt:

Lead time: How long does it take from idea to production?
Cycle time: How long does implementing and deploying a change take?
Deployment frequency: How often are changes safely deployed to production?
Change failure rate: What percentage of changes cause incidents or require hotfixes?
Bug/feature ratio: What proportion of work addresses bugs versus new capabilities?
Unplanned work: What percentage of engineering time goes to unplanned maintenance?
Comparative velocity: How does velocity compare to industry benchmarks?

2. Financial Impact Assessment

Translate technical indicators into financial terms:

Maintenance costs: What is the current cost of maintaining existing functionality?
Opportunity costs: What is the value of features delayed by technical constraints?
Operational inefficiency: What additional costs are incurred due to manual processes?
Infrastructure costs: Are there excessive infrastructure costs due to inefficient architecture?
Security risk exposure: What is the potential financial impact of security vulnerabilities?
Replacement costs: What would it cost to replace problematic systems?
Remediation timeline: How long would addressing critical debt take?

3. Risk Assessment

Identify and categorize risks stemming from technical debt:

Business continuity risks: What could cause significant system downtime?
Single points of failure: What components have no redundancy?
Knowledge risks: Where could the departure of key personnel be catastrophic?
Scalability limits: At what point will the current architecture fail to scale?
Security vulnerabilities: What are the most critical security gaps?
Competitive disadvantage: How does technical debt impact competitive position?
Regulatory risks: What compliance issues could arise from current practices?

4. Technical Debt Scoring

Develop a consistent scoring methodology:

Severity rating: How critical is each identified issue?
Scope impact: How many systems or features are affected?
Remediation difficulty: How complex would addressing the issue be?
Business impact: How significantly does the issue affect business outcomes?
Trend analysis: Is the debt increasing, stable, or decreasing over time?
Comparative benchmarking: How does the debt compare to industry peers?

Creating a Technical Debt Remediation Plan

A thorough evaluation should conclude with a prioritized action plan:

1. Debt Classification

Critical debt: Issues requiring immediate attention due to high risk or impact
Significant debt: Issues that should be addressed in the medium term
Acceptable debt: Issues where the cost of remediation outweighs benefits
Strategic debt: Issues requiring substantial architectural changes

2. Remediation Prioritization

Prioritize based on business impact and remediation cost
Balance fixing existing debt with preventing new debt
Identify quick wins that can show immediate value
Create a phased approach that aligns with business objectives

3. Establish Metrics and Monitoring

Define KPIs to track debt reduction over time
Implement automated measurements where possible
Create visibility through dashboards and regular reporting
Set realistic targets for improvement

4. Resource Allocation

Determine the investment required for debt reduction
Establish a sustainable allocation of resources to debt vs. features
Consider dedicated "fixathon" periods for focused debt reduction
Build technical debt awareness across the organization

5. Governance and Prevention

Implement architectural review processes
Establish coding standards and automated enforcement
Build technical debt considerations into planning
Create incentives for maintaining code quality
Foster a culture that values technical excellence

Case Studies: Technical Debt Assessment in Action

Startup Acquisition Due Diligence

A potential acquirer discovered that a startup's rapid growth had come at the cost of significant architectural debt. While the product had an impressive feature set, the assessment revealed:

A monolithic architecture struggling to scale
Test coverage below 30%, primarily focused on happy paths
Manual deployment processes prone to error
Critical knowledge concentrated in two key developers

The acquiring company adjusted the valuation to account for a 12-month remediation effort and made retention bonuses for key personnel a condition for the deal.

Enterprise Legacy System Modernization

A financial institution needed to assess whether to modernize or replace a 15-year-old core system. The technical debt evaluation revealed:

A stable but increasingly brittle COBOL-based system
Excellent test coverage but manual testing processes
Well-documented architecture but diminishing expertise in the technologies
Increasing maintenance costs consuming 80% of the IT budget

The assessment led to a phased modernization approach, beginning with infrastructure automation and gradually replacing components with modern services while maintaining the core functionality.

Open Source Integration Assessment

A healthcare company considering building on an open-source platform conducted a technical debt assessment of the codebase and found:

High-quality architecture with excellent separation of concerns
Inconsistent test coverage across modules
Active community addressing security issues promptly
Some modules with high contributor turnover indicating potential knowledge gaps

The company proceeded with the integration but allocated resources to improve test coverage in critical modules and assigned developers to become active contributors in areas strategic to their implementation.

Conclusion

Technical debt assessment requires looking beyond surface metrics to understand the true business impact of accumulated compromises. By systematically evaluating code quality, testing practices, infrastructure, technology stack, and development processes, you can accurately quantify technical debt and develop an effective remediation strategy.

The most successful technical due diligence combines quantitative metrics with qualitative assessment from experienced technical leaders who can interpret these findings in the broader business context. Remember that some technical debt is inevitable and even strategic—the goal is not debt elimination but rather conscious management of debt in service of business objectives.

By approaching technical debt assessment with thoroughness and nuance, organizations can make informed decisions about acquisitions, investments, and technology strategies that account for the complete picture of assets and liabilities in the digital realm.

Learn more at bluematter.io