How Code Quality and Scalability Audits Make or Break M&A Deals in Tech Acquisitions

You’re about to acquire a tech company. The financials look great. The pitch deck sparkles. But beneath that polished surface? The code might be a ticking time bomb. I’ve spent years in M&A tech due diligence, and I can tell you: code quality and scalability audits decide whether deals soar or sink. These audits aren’t just checkboxes – they’re your best tool to avoid costly surprises and negotiate with confidence.

Why Technical Due Diligence Can’t Be an Afterthought

Today, a tech company’s real value lives in its software, not its office furniture. Yet, too many deals zoom past the tech assessment. I see it all the time.

Take a recent deal: the seller promised a “world-class platform” with 2 million active users. Revenue was healthy. But when we got under the hood? The truth was messy:

• A monolithic architecture that choked with just 500 users online at once
• No automated tests – every check was done by hand
• Database queries so slow, API responses took over 5 seconds
• The main product stuck in the past, untouched for 14 months because “technical debt” made updates impossible

Without a real technical audit, the buyer would’ve inherited a house of cards. We used these findings to cut the purchase price by 40% – a direct result of the code quality and scalability issues we uncovered. This is the power of proper tech due diligence.

The Technical Due Diligence Process: What to Assess

Think of it like inspecting a house before buying. You need to see the foundation, not just the paint color. A good technical due diligence process checks:

• Code quality – Is it easy to work with, well-documented, following standards?
• Scalability – Can the system grow with your plans?
• Technology risk – Security holes, outdated tech, risky dependencies
• Team capability – Do they have the skills to build and maintain this?
• Product roadmap – Are their future plans actually buildable?

Let’s look at what each of these means in real deals. This is where the rubber meets the road.

Code Quality Audit: Beyond the Surface

Great code isn’t about perfect formatting. It’s about being maintainable, extendable, and reliable. I use automated tools, but I also read the code myself – because context matters.

Key Code Quality Indicators

Here’s what I watch for during a code quality audit:

• Cyclomatic complexity – Methods over 10? That’s a red flag for messy code
• Code duplication – Over 10% copied code? Often means shortcuts, not craftsmanship
• Unit test coverage – Below 70%? The codebase is likely fragile
• Documentation quality – Missing or outdated? New team members will struggle
• Build/test automation – Manual processes? That’s a deployment accident waiting to happen

I found this function in a recent payment system audit. It told a story of neglect:


// From a payment processing service
function processPayment(payment) {
// Added by John - 2020-03-15
// Modified by Sarah - 2020-06-22
// Fixed by Mike - 2020-11-08
// Temp fix for Black Friday - 2020-11-27
// Reverted Black Friday fix - 2021-01-15
// Quick fix for new tax rule - 2021-03-10

if (payment.amount > 1000) {
if (payment.currency === 'USD') {
if (new Date() > new Date('2022-01-01')) {
return processLargeUSDPaymentLegacy(payment);
} else {
return processLargeUSDPaymentNew(payment);
}
} else {
return processLargeForeignPayment(payment);
}
}

// Rest of function...
}


This wasn’t just one messy function. It was a pattern. The team kept patching instead of restructuring. The comments were a history of quick fixes, not solutions. In due diligence, this shows a culture of technical debt, not sustainable development. It’s a serious risk.

Automated Code Analysis Tools

Manual review is key, but tools give you breadth. My go-to stack:

• SonarQube – Finds bugs, vulnerabilities, code smells
• CodeClimate – Measures technical debt and maintainability
• Checkmarx – Security scanner for critical vulnerabilities
• GitPrime – Shows team productivity and collaboration patterns

One client hated the idea of running SonarQube. They were afraid of what it might find. But the results were critical: 85 open security issues, including SQL injection flaws in their public APIs. We used that data to renegotiate price and extend the integration timeline. The tool paid for itself many times over.

Scalability Assessment: Can the System Handle Growth?

Great code on paper means nothing if the architecture collapses under load. Scalability is about handling more users, more data, more features without breaking or blowing up your budget.

Architecture Evaluation Framework

When checking scalability, I ask:

• Monolith vs. microservices – Is the architecture the right fit for the product’s stage?
• Database design – Do they have a real plan for data growth?
• API response times – How slow do things get under stress?
• Infrastructure elasticity – Can resources grow automatically when needed?
• Caching strategy – Are they using caching to reduce load?
• Load testing results – Have they tested the system at the scale they claim?

A SaaS company once told us their platform handled “unlimited users.” Our performance test showed their single PostgreSQL database started slowing down with just 1,000 users online. Their “unlimited” was theoretical, not tested. That gap between claim and reality is common – and why real testing matters.

Real-World Scalability Testing

I always push for load testing during due diligence. At minimum, review their existing tests. If possible, run a quick test yourself. Tools I use:

• k6 – For stress-testing APIs
• Locust – To simulate real user behavior
• JMeter – For complex load scenarios

In one acquisition, the target projected 50% annual growth. Their diagrams showed a fancy load-balanced microservices setup. But our test revealed a fatal flaw: those “microservices” shared a single database connection pool. The entire system crashed at 2,000 users – far below the 10,000 they’d need in 18 months. Architecture diagrams don’t lie, but they also don’t always tell the whole truth.

Technology Risk Analysis: The Hidden Bombs

Technology risk analysis looks beyond code and load to find the long-term landmines. These are the issues that can derail your integration or sink the value post-deal.

Key Risk Areas to Investigate

I systematically check these areas during a technology risk assessment:

• Third-party dependencies – Open-source licenses, deprecated libraries, and security risks
• Vendor lock-in – Proprietary platforms that make migration expensive
• Security posture – Vulnerability history, security processes, and past breaches
• IP ownership – Are they actually the owner of the code they claim?
• Legacy technical debt – Old systems that should have been retired long ago
• Compliance risks – GDPR, HIPAA, or other industry-specific rules

One deal almost failed when we found the target’s core product used an AGPL-licensed library. Their modifications meant they had to open-source their entire product – contradicting their claim of all-IP ownership. This wasn’t a minor issue. It required a complete rewrite of the deal terms.

Technical Debt Assessment

Technical debt is often misunderstood. I break it down like this:

1. Visible debt – Issues documented in Jira, GitHub, etc.
2. Hidden debt – Problems shown by code analysis, not acknowledged
3. Interest payments – Time spent working around debt instead of building new features
4. Restructuring cost – Effort needed to fix the worst debt items

I reviewed one company with a 300+ item “technical debt backlog.” Digging deeper:

• 40% were critical security flaws
• 30% were performance fixes needed for growth
• 20% were usability issues hurting conversions
• Only 10% were truly minor

We calculated that cleaning the top half of that backlog would take 6-9 months of full-time engineering work. That became a major negotiation point for price and timeline.

Engineering Team Assessment: Beyond the Code

Tech due diligence isn’t just about the code. It’s about the people who build it. I evaluate the engineering team by looking at:

• Code ownership – The “bus factor” – how many people understand each critical system?
• Knowledge sharing – Documentation, cross-training, and onboarding processes
• Innovation capacity – How much time do they spend on new features vs. firefighting?
• Engineering culture – Code review quality, incident response, and blameless post-mortems

One red flag I watch for: “hero culture” – where 1-2 engineers are the only ones who understand the core systems. Lose them post-acquisition, and you lose the whole platform.

Red Flags and Green Flags

Red flags that often kill deals:

• No documentation – everything is tribal knowledge
• Bus factor of 1 – only one person knows key systems
• No automated testing or CI/CD – manual and risky deployments
• No security review process – blind to vulnerabilities
• No disaster recovery plan – vulnerable to outages

Green flags that build confidence:

• Strong documentation culture – everything is written down
• Structured code reviews and pair programming – knowledge is shared
• Mature CI/CD with automated tests – reliable and fast deployments
• Regular security audits and penetration tests – proactive security
• Disaster recovery drills and post-mortems – learning from incidents

Making Technical Due Diligence Work for You

Technical due diligence isn’t about finding reasons to walk away. It’s about getting the real story so you can make smart decisions. In my experience, the best outcomes happen when buyers:

1. Start early – Begin the tech assessment right after initial due diligence
2. Be thorough – Don’t trust vendor-provided information alone
3. Quantify risks – Turn findings into real cost and effort numbers
4. Use findings strategically – For price talks, integration planning, or risk mitigation

The most successful acquirers I know don’t just use tech due diligence to avoid bad deals. They use it to find the hidden value. In one recent case, our assessment showed the target’s platform had outstanding performance that the seller hadn’t capitalized on. The buyer didn’t just get a company – they got a strategic asset they could use across their entire business.

Remember: in tech M&A, the code *is* the company. A rigorous technical due diligence process isn’t optional. It’s how you know you’re getting what you pay for – and setting yourself up for success after the deal closes. It’s not just due diligence. It’s your best negotiating tool.

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