3 Code Quality Red Flags That Derail M&A Deals (And How To Spot Them)

Why Technical Due Diligence Makes or Breaks Your M&A Deal

When tech companies merge, the real story lives in the codebase. I’ve reviewed over 200 acquisition targets as an M&A consultant, and let me tell you – what looks solid on the surface often crumbles under scrutiny. Here’s what I want you to know: how a company handles these three code issues predicts whether your deal will sink or swim.

Code Quality Audit: Spotting Hidden Time Bombs

Think of me as your code detective. Just like authenticating rare collectibles, I look for these subtle warning signs that reveal a company’s true technical health:

1. The Legacy Code Nightmare

Last month, I found an e-commerce platform running on Perl scripts older than my teenager. The real issue? Their checkout system was duct-taped together with direct database calls:

# Recipe for disaster:
print "<td>$row->{'price'}</td>";

Versus teams doing it right:

class ProductService {
public function getPricingData() {
return DB::table('products')->select('price')->get();
}
}

What actually matters: Smart separation of old code from new systems beats “modern tech” claims every time.

2. Documentation Black Holes

A SaaS company once told me their code was “self-documenting.” Then we found this security-critical gem buried in middleware:

app.use((req, res, next) => {
if (req.ipRateLimit > 1000) {
return res.status(429).send('Enhance your calm');
}
next();
});

My rule of thumb: If more than 20% of files lack docs, you’re buying a mystery box.

3. Fake Test Coverage

That fintech startup boasting 95% test coverage? Turns out their payment tests were empty suits:

describe('Payment Processor', () => {
it('should handle chargebacks', () => {
// TODO: Maybe test money someday?
expect(true).toBe(true);
});
});

Green flag I love: Automated checks that actually block broken code from shipping.

Will This System Survive Growth?

Current performance means nothing if the architecture can’t scale. I always check:

Architectural Growing Pains

We use a simple 1-10 score assessing:

• Can they handle 10x traffic tomorrow?
• Is critical business logic trapped in one server?
• How easily can they split data across systems?

Real shocker: A media company handling 50K daily transactions would’ve collapsed at 500K due to hardcoded database limits.

Cloud Configuration Red Flags

Manual cloud setups are time bombs. I breathe easier seeing infrastructure-as-code like:

resource "aws_lb" "app_load_balancer" {
name = "acme-lb"
internal = false
load_balancer_type = "application"
security_groups = [aws_security_group.lb_sg.id]
subnets = [for subnet in aws_subnet.public : subnet.id]
}

No Terraform? Prepare for 2 AM scaling emergencies.

Deal-Killing Risks You Can’t Ignore

Some findings require immediate triage:

License Landmines

A single GPL violation once torched a $20M deal. We now automatically flag:

• Open source license conflicts
• Expired dependencies
• “Borrowed” code without attribution

Security Debt That Keeps Me Up

Our threat scoring model prioritizes real-world risks:

Turning Tech Truths Into Deal Terms

After 200+ technical audits, here’s my playbook:

• Fix critical issues first – perfection isn’t realistic
• Judge teams by how they respond to flaws, not flaws themselves
• Demand escrow for “tribal knowledge” systems

The best acquisitions happen when companies own their tech debt with clear repair plans. Because in M&A, what you don’t know about the codebase will hurt you.

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