Why Code Quality and Scalability Are Your Biggest M&A Deal Breakers (Or Makers)

The Critical Role of Technical Due Diligence in M&A

When one tech company buys another, you can’t skip the technical review. I’ll show you why a company’s approach to code and systems can either tank a deal or make it shine. As an M&A due diligence consultant, I’ve watched deals rise or fall based on one thing: the target’s technical backbone. Let’s look at what we always check: code quality, scalability, and tech risks.

What Is Technical Due Diligence?

Technical due diligence means reviewing a company’s tech stack, infrastructure, and how they build software. It’s not about ticking boxes. It’s about knowing if the tech can grow, integrate well, and avoid nasty surprises after the deal closes.

Why It Matters

I’ve seen too many deals fail because buyers ignored technical debt or overestimated scalability. Once, I worked on an acquisition where the target had great revenue. But their code was a mess—old systems, zero docs. The deal went through, but integration costs tripled. All the expected savings vanished.

Code Quality Audit: The Foundation of Value

A code quality audit checks if code is clean, easy to maintain, and efficient. We look for consistent style, good test coverage, and low technical debt.

Signs of Weak Code Quality

• Overly complex logic that’s hard to follow
• Test coverage under 80%
• Duplicated code or oversized classes

Here’s a Python example showing a common risk—skipping error handling:

def process_data(data):
# No error handling for empty or invalid data
return data.upper()

This works only when everything’s perfect. A stronger version:

def process_data(data):
if not data or not isinstance(data, str):
raise ValueError("Invalid input data")
return data.upper()

What You Can Do

Check test reports and use tools like SonarQube or ESLint. They automate reviews and save time.

Scalability Assessment: Planning for Growth

Scalability means your system can grow without breaking. We review database design, caching, and whether infrastructure can scale on demand.

Common Scalability Mistakes

• Monolithic apps that can’t scale in parts
• Slow database queries that create bottlenecks
• No auto-scaling in the cloud

A company might say they handle 10,000 users at once. But if their database has no indexes, performance will crumble under load.

Real-World Example

Take a MongoDB app. A query like this without an index:

db.users.find({ "email": "user@example.com" })

scans every record. Add an index:

db.users.createIndex({ "email": 1 })

and watch speed improve.

What You Can Do

Run load tests with Apache JMeter. Find weak spots before they hurt the deal.

Technology Risk Analysis: Finding Hidden Problems

Tech risk analysis covers security, compliance, and dependencies. We hunt for vulnerabilities, old libraries, and legal gaps.

Key Risks to Spot

• Unfixed security holes (like old OpenSSL versions)
• Missing compliance with GDPR or HIPAA
• Depending too much on one vendor or outdated tech

On one project, we found a target using an API that would shut down in six months. Fixing it was expensive, but finding it early helped us negotiate.

Code Snippet: Checking for Vulnerable Dependencies

For a Node.js project, run:

npm audit

It lists vulnerabilities and fixes. Simple but essential.

What You Can Do

Add dependency scans to your CI/CD pipeline. Keep risks in check continuously.

Making Due Diligence Work for Your Deal

Technical due diligence isn’t just a step—it’s a strategy. Review code quality, scalability, and risks thoroughly. You’ll avoid expensive shocks and find real value. Remember: a target’s tech can be an asset or a burden. Do the audits right, and your next deal will be a success.

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