How Code Quality Audits Reveal Hidden Value (or Risk) in M&A Tech Due Diligence

When one tech company buys another, a thorough technical review isn’t optional—it’s essential. I’ll show you how a target company’s code quality can be the silent dealmaker or breaker in M&A.

As an M&A due diligence consultant, I’ve assessed hundreds of tech companies across SaaS, fintech, and enterprise software. After years in this field, I can tell you: code quality and technical scalability often determine whether a deal succeeds or becomes a costly headache. A company’s approach to managing its codebase, scaling systems, and building risk controls into its engineering culture usually decides if the acquisition becomes a win or a write-off.

Why Code Quality Audits Matter More Than You Think

Most buyers start with financials, revenue, and customer numbers. These matter, but a code quality audit reveals what spreadsheets can’t—how sustainable, maintainable, and future-proof the technology really is.

I’ve watched promising deals fall apart when we discovered the codebase was a house of cards: undocumented, tightly coupled, and buried in technical debt. On the flip side, I’ve seen modest-looking startups with clean, modular codebases deliver triple the expected returns because they integrated smoothly with minimal refactoring costs.

What We Check During a Code Quality Audit

• Code readability and documentation: Do the comments actually help? Are there clear architecture diagrams? Can a new developer make sense of the system in a week?
• Test coverage and CI/CD maturity: Is testing automated? How quickly can they deploy changes?
• Tech debt measurement: What critical bugs exist? How many pull requests are open? Is there a plan to address tech debt?
• Security practices: Are secrets stored in the code? Do they scan dependencies? Are outdated libraries creating vulnerabilities?
• Code ownership and bus factor: Who can maintain each module? Is knowledge trapped with one person?

On a recent fintech deal, I found 60% of their transaction engine had no unit tests. Deployments required manual handoffs, and only the lead architect understood the reconciliation system. That’s not a “minor concern”—it’s a serious risk that should make any buyer pause.

Quick Action: Try Static Analysis First

Start with tools like SonarQube, CodeClimate, or GitPrime to get objective metrics. Pay special attention to:

• Cyclomatic complexity over 15 (indicates overly complex code)
• Code duplication above 10% (suggests poor modularity)
• Frequent changes to legacy modules (signals instability)
• Test coverage below 70% (below 50% is serious concern)

Here’s what one SonarQube report revealed in a recent audit:


Maintainability Rating: D (High technical debt)
Cyclomatic Complexity per method: 28 (avg)
Duplicated Lines: 14.3%
Test Coverage: 42%
Vulnerabilities: 12 (3 critical, 5 high)


This report alone convinced the buyer to lower their offer by 15% due to the integration challenges.

Scalability: Can the System Grow With You?

Scalability isn’t just about handling more users. It’s about how gracefully the system adapts to your growth plans, data volume increases, or global expansion needs.

Key Architecture Questions

We look closely at:

• Monolith vs. Microservices: Can components be separated? Or is the code a tangled mess?

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• Database design: Are there inefficient queries? Missing indexes? Any sharding for growth?
• Performance metrics: What’s the 95th percentile API speed? How does it handle double the traffic?
• Reliability features: Are there circuit breakers? Rate limiting? Graceful degradation?

One SaaS company bragged about “99.99% uptime” until our load testing revealed their API slowed to 3-second response times at twice their peak load. Worse, their database was a single point of failure with no backup. That’s not scalable—it’s a risk waiting to happen.

Simple Test: Simulate Real-World Load

Use k6 or Locust to test performance under realistic conditions:


// k6 script to test API scalability
export default function () {
const url = 'https://api.target.com/v1/users';
const res = http.get(url);
check(res, { 'status was 200': (r) => r.status == 200 });
}


If performance degrades sharply or fails, you’ve found a serious limitation. This is your chance to renegotiate or reconsider.

Technology Risk: Finding Hidden Problems in the Stack

Every tech stack has risks. In M&A, these risks can derail the entire deal. We look for three main categories:

1. Dependency Risks

Watch for:

• Unmaintained open-source libraries?
• Outdated frameworks (like AngularJS or Python 2)?
• Third-party tools with expensive lock-in?

One target used a legacy license system costing $200K annually—and couldn’t switch without a major rewrite. This hidden cost wasn’t reflected in their financials but would hit the new owner hard.

2. IP and Licensing Issues

Is the code truly theirs? Check for:

• GPL code in a commercial product?
• Unattributed open-source components?
• Code taken from previous employers?

For these, we use tools like FOSSA or WhiteSource. One deal fell apart when we found 40% of their code was under GPL, which would force the entire product to become open-source post-acquisition.

3. Talent and Knowledge Gaps

Ask: Can the system survive if key people leave? If the CTO walks out, can others maintain the product?

We map code ownership using Git history. If one person wrote 70% of the code, that’s a bus factor of 1—a major integration risk.

From Red Flags to Green Lights

The best targets don’t hide problems—they document and address them. I look for teams that:

• Maintain a public tech debt list with clear priorities
• Use modern CI/CD pipelines with automated security checks
• Have a scalability plan with concrete milestones
• Foster knowledge sharing through documentation and collaboration

One startup I reviewed had 80% test coverage, daily deployments, and an open Slack channel for architecture discussions. Their code quality was their advantage. The buyer paid a 30% premium and integrated them twice as fast as expected.

Real-World Lessons: What “Cherrypick” Really Means in Tech M&A

In coin collecting, a “cherrypick” means finding rare value. In tech M&A, it’s about spotting either undervalued strengths or overlooked risks.

• Clean, well-documented code? Cherrypick.
• Teams that share knowledge through documentation? Cherrypick.
• Systems that handle growth smoothly? Cherrypick.
• One person holding the system hostage? Red flag.
• Unfixed security flaws in production? Time to step away.

Final Thought: Code Quality Is the New Due Diligence Standard

In tech M&A, the codebase is the real asset. Financials show past performance. The code, architecture, and engineering culture reveal what’s possible next.

My advice to every acquirer: Never skip the code quality review. Run static analysis. Test under load. Map dependencies. Verify IP cleanliness. And most importantly, talk to the engineers—not just the executives.

The best acquisitions aren’t just buying a product. They’re investing in a system that will scale, integrate, and create value long after the deal closes. That’s the real win.

When you walk into the dataroom, bring more than financial models. Bring a checklist, a load tester, and genuine curiosity about the code. Because in tech M&A, the future of the deal lives in the code.

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