Acquiring a software company without reviewing its code is like buying a factory without inspecting the machinery.
The revenue might seem impressive. Customer growth might look strong. Financial statements might appear solid. But hidden within the software can be millions of dollars in technical debt, security vulnerabilities, scalability problems, or even intellectual property risks.
This is why a software due diligence checklist is important. For investors, private equity firms, venture capital funds, and M&A teams, software is often the most important asset in a deal. Yet many acquisitions still focus on financial and legal checks while neglecting technical review. That’s a mistake.
A poorly maintained codebase can greatly lower post-acquisition ROI. In some cases, it can disrupt integration, raise engineering costs, or put the buyer at risk of regulatory and security issues.
This guide presents a practical software due diligence framework to help investors evaluate:
• Code quality
• Technical debt
• Security vulnerabilities
• Software architecture
• AI-generated code risks
• Ownership and IP issues
Services like The Code Registry help investors and executives translate complex source code into clear business risk insights, making technical reviews easier for non-engineering stakeholders.
What Is Software Due Diligence?
Software due diligence is the process of evaluating a software company’s source code, architecture, development practices, security, scalability, and technical risks before making an acquisition, investment, or strategic partnership.
Its main goal is clear: determine if the software asset is healthy, scalable, secure, maintainable, and worth the proposed valuation.
Software due diligence usually looks at:
• Code quality
• Technical debt
• Architecture
• Infrastructure
• Security
• DevOps maturity
• Open-source dependencies
• IP ownership
• Risks from AI-assisted code generation
Think of it as a technical health check of the business’s main digital asset.
Why Software Due Diligence Matters in M&A
Many investors underestimate how often software problems turn into business problems. A company may report:
• $20M ARR
• 90% retention
• Rapid growth
Everything looks excellent. However, technical diligence may uncover:
• Legacy monolith architecture
• Critical security vulnerabilities
• Massive code duplication
• Single-developer dependency
• Unsupported frameworks
Now the acquisition appears very different. Why? Because software issues impact business outcomes.
Common Business Impacts of Poor Software Health
Technical issues often translate directly into:
- Increased post-acquisition investment
- Slower product velocity
- Reduced EBITDA margins
- Lower valuation
In real M&A scenarios, technical debt can reduce enterprise value by millions.
When Should Software Due Diligence Begin?
Short answer:
Earlier than most teams think.
Ideally, software due diligence begins:
Before signing the LOI (Letter of Intent)
or
Immediately after preliminary financial validation
Waiting until late-stage diligence creates problems.
Why?
Because by then:
Valuation expectations are already set.
Negotiation leverage decreases
Discovery becomes expensive
Best practice:
Diligence Timeline
Stage 1 — Early Screening
High-level technical assessment.
Questions:
Is the stack modern?
Are obvious risks visible?
Stage 2 — Deep Technical Review
Full code audit and architecture analysis.
Questions:
How healthy is the code?
What remediation costs exist?
Stage 3 — Final Risk Adjustment
Adjust valuation based on technical findings.
Questions:
Is acquisition pricing still justified?
The Complete Software Due Diligence Checklist
Here is the 10-point framework investors should use.
- Code Quality Assessment Code quality determines maintainability and engineering efficiency.
Good code compounds value.
Bad code compounds cost.
Evaluate:
Code complexity
Duplication
Naming consistency
Maintainability
Test coverage
Refactorability
Questions to Ask
Is the code readable?
Is the architecture clean?
Are coding standards enforced?
Is there excessive duplication?
Red Flags
Spaghetti code
Giant files
No tests
Overly coupled modules
Metrics to Review
Cyclomatic complexity
Code duplication %
Test coverage %
Maintainability score
This is where code intelligence platforms like The Code Registry become valuable—they quantify code quality into measurable risk indicators.
- Technical Debt Assessment Technical debt is one of the most underestimated acquisition risks.
Not all technical debt is bad.
Strategic debt can accelerate growth.
Unmanaged debt destroys velocity.
Technical debt includes:
Legacy code
Deferred refactoring
Outdated libraries
Architectural shortcuts
Manual operational work
Questions to Ask
- How much refactoring is overdue?
- Which systems are hardest to maintain?
- What modernization costs exist?
Red Flags
- End-of-life frameworks
- High bug recurrence
- Slow releases
- Frequent production incidents Ask a practical question:
If the current engineering team disappeared tomorrow, how hard would this system be to maintain?
That answer often reveals the real debt.
- Security Review Security issues can destroy deal economics overnight.
Security diligence should include:
Source code vulnerabilities
- Dependency vulnerabilities
- Secrets exposure
- API risks
- Authentication weaknesses
- Access control flaws
A strong framework is the OWASP Top 10.
Security Checklist
Review for:
- Injection vulnerabilities
- Broken authentication
- Sensitive data exposure
- Dependency risks
- Hardcoded secrets
Questions to Ask
- How often are scans performed?
- Are vulnerabilities remediated quickly?
- Is secure coding practiced?
Red Flags
- No security scans
- Critical CVEs unresolved
- Public secrets in repositories
One critical vulnerability can materially impact valuation.
- Architecture Review The Architecture of a system dictates its ability to scale.
The question isn't, "Does the product work today?" The real question is: can it support 10x growth?
Assess:
- Modularity
- Scalability
- Resilience
- Fault tolerance
- Performance
- Observability
Questions to Ask
- Can traffic scale easily?
- Are there single points of failure?
- Is architecture documented?
Red Flags
- Monolithic bottlenecks
- Tight coupling
- No observability
- Fragile integrations
Poor architecture increases future infrastructure and engineering costs.
- Infrastructure Assessment
Software does not run alone. Infrastructure is important.
Review:
- Cloud setup
- Hosting
- Backup strategy
- Disaster recovery
- Monitoring
- Cost efficiency
Questions to Ask
- Are backups tested?
- Is disaster recovery documented?
- Are cloud costs optimized?
Red Flags
- No DR plan
- Manual deployments
- No infrastructure-as-code
- Weak monitoring
- DevOps & SDLC Review
A codebase may look healthy on paper but still be operationally fragile. This is where DevOps comes into the picture.
Software teams with strong engineering discipline typically have:
- Automated testing
- CI/CD pipelines
- Rollback mechanisms
- Monitoring and alerting
- Reliable deployment processes
Teams without these systems usually miss deadlines, and the final product may break more often.
DevOps Checklist
- CI/CD implementation
- Deployment frequency
- Mean time to recovery (MTTR)
- Incident response
- Release rollback capability
- Test automation maturity
Questions to Ask
- How frequently are releases deployed?
- Is deployment automated or manual?
- How quickly can failures be rolled back?
- Are incidents tracked postmortem?
Red Flags
- Manual deployments
- No staging environment
- No automated testing
- Frequent release failures
A practical observation:
When releases depend on “that one engineer who knows production,” operational risk is high.
- Open Source License Review
This section often gets overlooked. That’s risky. Modern software depends a lot on open-source components. Many companies use hundreds or even thousands of dependencies. Not all licenses are the same. Some have legal or commercial restrictions.
*Review: *
- Dependency inventory
- License types
- Version support
- Vulnerability status
*Common license types: *
- MIT
- Apache 2.0
- BSD
- GPL
- LGPL
*Questions to Ask *
- Are all dependencies tracked?
- Are licenses acceptable for commercial use?
- Are unsupported libraries still in use?
*Red Flags *
- Unknown dependency inventory
- GPL contamination risks
- Unsupported libraries
- No SBOM (Software Bill of Materials)
For enterprise acquisitions, failing to comply with licensing requirements can lead to serious legal problems.
- Documentation Review
Documentation is often treated as optional.
It isn’t.
Poor documentation increases:
- Onboarding time
- Knowledge dependency
- Incident resolution time
- Transition risk
Review:
- Architecture documentation
- API documentation
- Deployment runbooks
- Incident response docs
- Engineering SOPs
Questions to Ask
- Is documentation current?
- Can a new engineer become productive quickly?
- Are operational processes documented?
Red Flags
- No architecture diagrams
- Tribal knowledge only
- Outdated runbooks
- Missing API references
Here’s a useful diligence test:
Could another engineering team take over this system in 30 days?
If the answer is no, the documentation risk is significant.
- Ownership & IP Validation
This is one of the highest-risk areas.
Many buyers assume the company owns all source code.
Sometimes it doesn’t.
Check:
- Employment agreements
- Contractor agreements
- Contributor agreements
- Third-party ownership clauses
Questions to Ask
- Was the code written by contractors?
- Are IP assignments signed?
- Any disputed ownership?
Red Flags
- Missing contractor agreements
- Offshore development without IP clauses
- Open-source license contamination
- Shared code across companies
If ownership is unclear, acquisition risk rises sharply.
Remember:
Owning the company does not automatically guarantee clean ownership of all code.
- AI-Generated Code Assessment This is increasingly critical in 2026.
AI-assisted development has dramatically changed software engineering.
Tools like:
- GitHub Copilot
- Cursor
- Claude Code
- ChatGPT coding assistants
…can accelerate development significantly.
But they introduce new risks.
AI Code Risks
- Hallucinated logic
- Security vulnerabilities
- License contamination
- Undocumented behavior
- Hidden dependencies
Questions to Ask
- What percentage of code was AI-assisted?
- Are AI-generated commits tracked?
- Is AI output reviewed?
- Are governance policies defined?
AI Code Review Checklist
Evaluate:
- Human review process
- Security scanning
- Ownership verification
- Dependency analysis
- Code provenance
This is where The Code Registry has a major market advantage.
Most traditional diligence frameworks ignore AI-generated code entirely.
That is increasingly becoming a blind spot.
Top Red Flags Investors Should Never Ignore
Here are the biggest warning signs.
- Single developer dependency
- No automated tests
- Massive technical debt backlog
- Unsupported frameworks
- No security scanning
- Poor documentation
- High code duplication
- Unknown AI-generated code usage
- No IP assignment agreements
- No disaster recovery plan
If you see 4 or more of these, proceed cautiously.
Software Due Diligence Scoring Framework
Not every risk carries equal weight. A missing architecture diagram is inconvenient. A critical security vulnerability is potentially catastrophic.
That’s why investors should use a weighted scoring model.
Here is a practical framework.
Total = 100%
Scoring Bands
This framework helps turn technical complexity into business language.
That matters because boards and investors don’t buy “clean code.”
They buy:
- future cash flows
- scalability
- defensibility
- reduced risk
How The Code Registry Helps
For many investors, the hardest part of technical diligence isn’t access to code.
It’s interpretation.
Most stakeholders in an acquisition are not software architects.
They need answers to business questions:
- Is this software healthy?
- What risks exist?
- How much technical debt is present?
- How expensive is remediation?
- Will this scale after acquisition?
This is where The Code Registry provides significant value.
The platform helps organizations convert raw code into actionable business intelligence.
Instead of handing investors 500-page engineering reports, solutions like The Code Registry surface critical insights such as:
- Code health scores
- Technical debt indicators
- Architecture risks
- Security exposure
- AI-generated code risks
- Software valuation signals
That makes technical diligence understandable for:
- CEOs
- Investors
- Board members
- M&A teams
More importantly, it improves decision quality.
Final Recommendations for Investors
If you’re evaluating a software company, these five recommendations matter most.
- Start Technical Diligence Early
Don’t wait until the deal is nearly complete.
Earlier discovery gives negotiation leverage.
- Quantify Technical Debt
Technical debt should be measured financially.
Ask:
What is the remediation cost?
That’s the number that matters.
- Don’t Ignore AI-Generated Code
AI-assisted coding is now mainstream.
But many organizations lack governance.
This creates blind spots.
Review:
- AI usage
- code provenance
- review processes
- Validate IP Ownership
Legal ambiguity around code ownership can kill deals.
Verify everything.
Especially contractor contributions.
- Use Independent Code Intelligence
Founders naturally present their systems in the best light.
Independent analysis creates objectivity.
That’s critical for investment decisions.
Conclusion
Software is often the most valuable asset in a modern technology company.
Yet it is also one of the least understood during acquisitions.
That mismatch creates risk.
A strong software due diligence checklist helps investors move beyond surface-level metrics and understand the true health of the underlying software asset.
The best acquirers don’t just ask:
- How much revenue does this company generate?
They also ask:
- How maintainable is the code?
- How secure is the architecture?
- How scalable is the platform?
- What hidden liabilities exist?
These questions can determine whether a deal becomes a success—or an expensive mistake.
If you’re evaluating a software acquisition, investment, or strategic partnership, independent technical assessment can provide the clarity needed to make better decisions.
Planning an acquisition or investment?
Request a Software Due Diligence Assessment from The Code Registry to evaluate software quality, technical debt, security risks, and AI-generated code exposure before capital is committed.
Frequently Asked Questions (FAQs)
- What is software due diligence?
Software due diligence is the process of evaluating a software company’s codebase, architecture, security, scalability, and technical risks before acquisition, investment, or strategic partnership. Its goal is to identify hidden technical liabilities that may affect valuation or post-deal performance.
- Why is software due diligence important in acquisitions?
Software due diligence helps investors uncover technical debt, security vulnerabilities, poor architecture, and operational risks that are not visible in financial statements. These issues can significantly impact deal value and post-acquisition costs.
- Who performs software due diligence?
Software due diligence is typically performed by:
- Technical due diligence consultants
- Software architects
- Security specialists
- Code intelligence platforms like The Code Registry
The ideal reviewer combines technical depth with business understanding.
- How long does software due diligence take?
Typical timelines:
Duration depends on:
- Codebase size
- System complexity
- Documentation quality
- Team availability
- What is included in a software due diligence checklist?
A comprehensive checklist includes:
- Code quality assessment
- Technical debt analysis
- Security audit
- Architecture review
- Infrastructure review
- DevOps maturity
- Documentation quality
- IP ownership verification
- AI-generated code assessment
- How does technical debt affect valuation?
Technical debt reduces valuation because it increases future engineering costs.
For example, if remediation requires $1M in engineering investment, buyers often factor that into negotiations, reducing acquisition price or demanding protections.
- What are common red flags during technical due diligence?
Major red flags include:
- No automated tests
- Poor documentation
- Legacy frameworks
- Security vulnerabilities
- High code duplication
- Single developer dependency
- Unknown AI-generated code usage
- Can AI-generated code create risks?
Yes.
AI-generated code can introduce:
- Security flaws
- Hallucinated logic
- License risks
- Hidden dependencies
- Governance issues
Organizations should establish AI code review policies and audit AI-assisted code regularly.
- What tools help with software due diligence?
Common tools include:
- Static code analysis tools
- Dependency scanners
- Security scanners
- Architecture review tools
- Code intelligence platforms
Platforms like The Code Registry help translate technical signals into business-readable risk insights.
- How much does software due diligence cost?
Cost varies based on complexity.
Typical ranges:
Cost should be compared against potential downside risk from poor acquisitions.
