How Code Quality Audits Can Make or Break Your M&A Deal: Insights from a Tech Due Diligence Consultant

Buying or merging with another tech company? The numbers on paper don’t tell the whole story. As someone who’s spent years in the trenches of M&A tech due diligence, I can tell you: a company’s code and systems matter just as much as its revenue. I’ve seen promising deals nearly derail over messy codebases – and others saved because a technical audit revealed hidden strengths.

Understanding the Importance of Technical Due Diligence in M&A

In M&A, spreadsheets get all the attention. But the real story? It’s in the code. I’ve watched brilliant financial deals nearly collapse because the acquiring company skipped the technical homework. Technical due diligence isn’t about playing catch – it’s about spotting landmines *before* you step on them, finding the real value, and making sure the combined tech actually works together.

The Three Pillars of Technical Due Diligence

My checklist for any tech audit boils down to three things:

• Code Quality Audit: Can the codebase be maintained, scaled, and trusted?
• Scalability Assessment: Can this system handle growth, or will it choke?
• Technology Risk Analysis: What hidden problems, debt, or integration headaches are lurking?

Let’s break down each one, with real stories from the field and practical steps you can use.

Code Quality Audit: Is This Code Worth Inheriting?

The codebase is the company’s backbone. Bad code means expensive fixes, slow product cycles, and frustrated engineers. A code quality audit in M&A is like a home inspection – it reveals whether you’re buying a solid foundation or a money pit.

Key Metrics for Code Quality Assessment

Here’s what I look for in code quality:

• Code Complexity: High complexity (like cyclomatic complexity) means spaghetti code that’s hard to change
• Coupling and Cohesion: Good systems keep things separate but well-organized
• Test Coverage: Tests mean fewer surprises and safer updates
• Code Smells and Anti-Patterns: Warning signs of deeper design flaws

Just last year, I audited a company where the average cyclomatic complexity was 15 – but some payment modules hit 45. (Anything over 20 starts to get brittle). SonarQube flagged this:


Complexity per file:
- user_service.js: 18
- payment_processor.py: 45
- inventory_api.go: 22
- recommendation_engine.js: 38

Test Coverage:
- Overall: 58%
- Critical paths: 42%


That 42% test coverage on core payment code? It wasn’t pretty. We used this to knock 20% off the price. Fixing that debt would cost real money and time.

Actionable Takeaway: Make Code Quality Non-Negotiable

Buyers should enforce code standards *before* the deal closes. Here’s how:


# Example .github/workflows/ci.yml
name: CI
on: [push, pull_request]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Run SonarQube analysis
uses: sonarsource/sonarqube-scan-action@master
env:
SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
SONAR_HOST_URL: ${{ secrets.SONAR_HOST_URL }}
- name: Check Coverage
run: |
# Fail if coverage is below 80%
if [ $(cat coverage.txt) -lt 80 ]; then
echo "Test coverage too low!"
exit 1
fi


These automated checks keep code quality high *during* development. No more last-minute surprises.

Scalability Assessment: Can This Tech Grow With the Business?

You’re buying for growth. But can the tech handle it? I’ve seen companies with great products fail to scale because their system hit a wall at 10x the users. A scalability assessment answers: *Can this handle our plans?*

Key Aspects of a Scalability Assessment

Here’s what I investigate:

• Architecture Patterns: Is it a monolith or microservices? How are components connected?
• Data Storage Solutions: What database? Sharded? Cached? Ready for more data?
• Performance Benchmarks: What happens under heavy load? What’s the latency?
• Resource Utilization: How much CPU, memory, and network does it use when busy?

One client wanted to buy a company with 100K users. Their system was a clean monolith with a single MySQL database. Fine for now. But they planned to hit 1M+ users post-acquisition. The math didn’t work. We recommended splitting into microservices with a distributed database. Added $2M to the post-deal budget – but prevented a potential meltdown.

Actionable Takeaway: Test It Under Real Pressure

Theories are cheap. Real data matters. Run load tests with tools like k6:


// Example k6 load test script
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
stages: [
{ duration: '5m', target: 100 }, // Ramp up to 100 users
{ duration: '10m', target: 1000 }, // Maintain 1000 users
{ duration: '5m', target: 0 }, // Ramp down
],
};

export default function () {
const res = http.get('https://api.targetcompany.com/v1/users');
check(res, { 'status was 200': (r) => r.status == 200 });
sleep(1);
}


These tests expose bottlenecks. Use them to plan integration *before* you sign.

Technology Risk Analysis: Finding the Hidden Landmines

Every tech stack has risks. The goal isn’t to find a perfect system (they don’t exist) – it’s to find the *manageable* risks and price them in. I’ve been the guy who found the Log4j vulnerability buried in a Java app three days before closing. That kind of discovery changes the conversation.

Common Technology Risk Areas

Here are the risks that keep me up at night:

• Security Vulnerabilities: Old libraries, known CVEs, weak security habits
  • Example: That unpatched Log4j issue I found? Saved the buyer $500K in emergency fixes
• Licensing Compliance: Illegal open-source use, expired licenses
  • Example: GPL code in a proprietary product? Cue the legal headaches
• Vendor Lock-in: Stuck on one cloud or proprietary tech
  • Example: AWS Lambda-only system makes multi-cloud impossible
• Technical Debt: Years of shortcuts that will cost big money later
  • Example: No tests? Every release becomes a roll of the dice

Actionable Takeaway: Make Risks Visible and Actionable

I always build a risk matrix to prioritize:


| Risk | Likelihood (1-5) | Impact (1-5) | Mitigation Strategy | Owner | Timeline |
|------|------------------|--------------|---------------------|-------|----------|
| Outdated dependencies | 4 | 3 | Update to latest versions | DevOps | 30 days |
| No automated testing | 5 | 4 | Implement CI/CD with test suite | QA Lead | 60 days |
| Single cloud provider | 3 | 5 | Design multi-cloud strategy | Architecture | 90 days |


This isn’t just a list – it’s a plan. It shows what needs to be fixed, who fixes it, and when.

Putting It All Together: The Big Picture

Code quality, scalability, and risk aren’t separate issues. They’re connected. A system can have beautiful code but crumble under load. Or it can scale easily but have huge security gaps. The real work is understanding how these pieces fit together – and how they’ll work with *your* company.

The Integration Factor

One thing people miss? How the two companies actually integrate. Ask:

• How do their systems connect to ours?
• Do our engineers know the same tech? Or is there a skills gap?
• Will their culture and processes work with ours?
• How do we merge the data cleanly?

I audited a company with stellar code – but built entirely in Elixir. My client’s team only knew Python and Java. The integration would cost more and take longer. We built that into the valuation.

Actionable Takeaway: Score the Deal

I use a scorecard to track everything:


| Category | Score (1-10) | Notes |
|----------|--------------|-------|
| Code Quality | 7 | Good coverage but high complexity |
| Scalability | 5 | Monolithic architecture, needs refactoring |
| Security | 8 | Up-to-date dependencies, good practices |
| Integration | 6 | Elixir skills gap, but good API design |
| Overall | 6.5 | Potential with investment |


This simple table? It’s how we negotiate. It shows the strengths, the weaknesses, and the cost of fixing them.

Conclusion: Tech Due Diligence Is Your Secret Weapon

I’ve been in rooms where tech due diligence changed the entire deal. We’ve walked away because the risks were too high. We’ve renegotiated prices because the code was a mess. And we’ve bought companies at great prices because the tech was rock-solid.

This isn’t about finding flaws. It’s about finding *reality*. A company with okay code but massive growth potential? That’s a smart buy. A company with perfect code but a tech stack that can’t scale? That’s a problem.

Key things to remember:

• Set code quality gates in their CI pipeline *now*
• Test scalability with real-world load tests
• Build a risk matrix with clear owners and timelines
• Think about integration from day one – tech, people, and process
• Use a scorecard to track the total picture

Tech due diligence isn’t just a checklist. It’s the difference between a deal that works and one that becomes a nightmare. In M&A, the best decisions are made with eyes wide open – and that starts with understanding the code.

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