How Code Quality Audits Uncover Hidden Tech Risks in M&A Deals: A Due Diligence Consultant’s Red Flags & Green Lights

You’re about to buy a tech company. The pitch deck looks great. Revenue is growing. The team seems solid. But before you sign, ask yourself: *What’s really under the hood?*
I’ve spent years auditing code in M&A deals. And more often than not, the real story isn’t in the financials — it’s in the codebase. What I find there can make or break the deal.

The Hidden Risks in Code Quality During M&A Due Diligence

I’ve watched promising acquisitions unravel because the code was a mess. Not just “needs improvement” messy — we’re talking systems held together by duct tape and hope.
Poor code quality doesn’t just slow things down. It hides in plain sight, then bites you later. Massively.

The reality: A business with a great product and terrible code can cost buyers millions to fix. Missed deadlines. Resentful developers. Abandoned features. All because no one looked closely at the code before the deal.

What to Look for in a Code Quality Audit

• Code Review Processes: Do they actually review code, or does everyone just push straight to main? Real pull requests? Meaningful feedback? Automated checks? These matter.

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• Test Coverage & CI/CD: Are tests real tests, or just placeholders? Is deployment a 2-hour manual process, or a one-click release?

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• Documentation: Is there any? Not just “readme files,” but actual context — why this design, how this service fits, what’s the plan?
• Code Smells & Anti-Patterns: Long functions. God classes. Circular dependencies. Magic strings. These aren’t quirks — they’re warning signs.
• Third-Party Dependencies: Are they using libraries from 2016 with no updates? Known CVEs? No license? That’s a risk I see *all* the time.

Red Flag: The “Big Ball of Mud” Architecture

I once audited a SaaS startup with 50K+ active users. Revenue was climbing. The team was passionate. Everything *seemed* perfect.
Then I opened the main service file.
A single function. 200+ lines. Handling auth, business logic, database calls, logging, and email — all in one tangled mess.

// This function does everything: user auth, DB queries, logging, and email
function handleUserRequest(req, res) {
// ... 200+ lines of mixed logic ...
if (req.user.role === 'admin') {
// ... more mixed logic ...
if (req.body.action === 'delete') {
// ... even more ...
}
}
// ... and so on ...
}

No modularity. No separation. No tests. Just chaos.
Every change risked breaking something. Every bug took hours to trace. This wasn’t just “technical debt” — it was a rewrite waiting to happen.
We told our client: pause the deal. The cost to fix this would eat into the upside. They renegotiated — and got the right price.

Scalability Assessment: Can the Tech Stack Handle Growth?

Good code isn’t enough. Can it *scale*?
I’ve seen companies with pristine code that couldn’t handle a 2x traffic spike. Clean doesn’t mean resilient.

Key Questions in a Scalability Assessment

• How’s the data stored? Are there slow queries or hot spots in the database?
• Is the system stateless? Can you add new servers without breaking sessions?

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• Microservices or monolith? If it’s microservices, do they actually talk clearly — or is it just a monolith cut into pieces?
• How are tasks handled asynchronously? Queues? Cron jobs? Do they time out?
• What do real load tests show? Not just “it works,” but under 3x, 5x, 10x traffic?

Green Light: Microservices with Clear Boundaries

Another company I reviewed had a different story. Clear domain boundaries. Each microservice owned its data. Docker containers. Kubernetes orchestration.
Their load tests? Solid performance at 10x current traffic. No bottlenecks.
This wasn’t just scalable — it was *extendable*. The buyer could plug in new features, new teams, new markets — without fear of collapse.
That’s what a green light looks like.

Technology Risk Analysis: Beyond Code and Architecture

Code and architecture matter. But so do the *people* and *systems* behind them.
A beautiful codebase is useless if no one can maintain it. Or if it depends on a library that got abandoned in 2018.

Hidden Risks in Tech Due Diligence

• Team Skills: Does the team actually know the stack? Or are they faking it? Is there one person who holds it all together?

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• Third-Party Dependencies: Are they using open-source tools with security holes? No updates? No maintenance?

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• Compliance & Security: Are they handling PII right? Data retention policies? Has anyone done a real security review?

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• Vendor Lock-In: Is everything on one cloud? One SaaS platform? Can they move if they need to?
• Intellectual Property: Do they *own* the code? Or did a contractor write it and walk away with rights?

Red Flag: The “Hero Developer” Syndrome

One company I audited built their entire product — a complex analytics platform — from scratch. By one person. Who left six months earlier.
The code? Clean. Well-written. Even had tests.
But the new team had no docs. No onboarding. No idea how anything really worked.
That’s not technical debt. That’s technical orphanhood.
We told the buyer: walk away. Or budget for a full rebuild. The risk wasn’t just delay — it was continuity. And that’s too much to gamble on.

Actionable Takeaways: How to Conduct a Tech Due Diligence Audit

Here’s how I approach these audits — with real-world results.

1. Define the Scope

Don’t audit everything. Focus on what matters.
Is it a fast-scaling startup? Prioritize scalability and architecture.
Is it a legacy system? Focus on maintainability and dependencies.
Tailor the audit to the business, not the other way around.

2. Gather Evidence

• Code Review: Use tools like SonarQube or ESLint, but don’t trust them blindly. Read the code. Spot the patterns. Look for the exceptions.
• Architecture Review: Ask for diagrams. Real ones — not marketing fluff. Get load test data. Deployment scripts. CI/CD pipelines.
• Team Interviews: Talk to the CTO. The lead dev. The ops engineer. Ask: “What keeps you up at night?” Their answers matter more than their resumes.
• Dependency Audit: Run Snyk or Dependabot. Find old packages. Vulnerable ones. Ones with no license. These are ticking time bombs.

3. Identify Risks and Mitigation Strategies

Don’t just list risks. Say what they *mean*.
For example:

• Risk: Core module has no tests and high complexity.
• Impact: One bug fix could break the system. 6–12 months to stabilize.
• Mitigation: Reduce offer by 15–20%. Require pre-acquisition fixes. Or plan a phased rewrite.

4. Report and Recommend

Be clear. Be specific.
“Proceed, but with a 20% valuation discount for code cleanup.”
“Walk away unless they fix the security gaps in 90 days.”
No vague “concerns.” Give the client a decision, not a headache.

5. Post-Acquisition Integration Plan

Tech due diligence doesn’t end at signing.
How will you merge systems? Align teams? Set 30-, 60-, 90-day goals?
The best audits don’t just find problems — they help build the path forward.

Conclusion: The Due Diligence Mindset

This isn’t about perfection. It’s about *clarity*.
You don’t need a flawless codebase. You need to know what you’re buying — and what it’ll cost to make it work.
After years in the trenches, I’ve learned: the code tells the truth. The business model might shine. The pitch deck might dazzle. But the code? It doesn’t lie.
Spend the time. Ask the right questions. Look under the hood.
Because in M&A, the right price isn’t just fair — it’s informed. And that starts with a real audit of the tech.
Whether you’re a buyer, a seller, or an advisor — don’t skip this. The risks are too high. The opportunities, too real.

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