How ‘Sort of Sorted’ Code Reveals Critical M&A Risks in Tech Due Diligence

When Technical Debt Sinks Deals: A Due Diligence Wake-Up Call

Picture this: your acquisition target looks perfect on paper. Revenue? Strong. Market position? Solid. Then you start scrubbing their codebase. Suddenly, that shiny $150M deal starts looking riskier than a Jenga tower in an earthquake. After 15 years unpacking tech stacks during acquisitions, I’ve learned one truth: how teams organize their code predicts more about integration pain than any financial spreadsheet.

The ‘Sort of Sorted’ Problem: More Dangerous Than You Think

Reading Between the Code Lines

Last quarter, I assessed a SaaS company that seemed acquisition-ready. Then we opened their repositories:

• Tests covering barely half their functionality (58% isn’t a passing grade)
• Folders named with three different conventions – like kitchen drawers sorted by someone half-asleep
• A database that evolved through four different architectural eras

This “sort of sorted” approach hid landmines beneath surface-level order. Imagine finding both vintage wine and expired milk in a “pantry organization” system – that’s what mixed-quality code looks like during M&A tech reviews.

Why Microscopes Beat Magnifying Glasses

One engineer on my team puts it perfectly: “You don’t glance at code during diligence – you biopsy it.” Watch for patterns like this:

// Warning Sign: Identity crisis in a single function
function processUser() {
validateEmail(user.email); // Good hygiene
db.query('UPDATE stats SET last_login=NOW()'); // Why's this here?
if (user.age > 13) { /* ... */ } // Hardcoded rules = future headaches
}

Our Field-Tested Diligence Framework

1. Architecture Health Check

We score three key areas that predict integration nightmares:

• Service dependencies (keep them lean – under 15%)
• Shared code usage (40-60% is the sweet spot)
• Design pattern consistency (no mixing monoliths and microservices like stripes with polka dots)

2. Scalability Autopsies Reveal Hidden Truths

A video platform we reviewed had “mostly automated” scaling. Their config told a different story:

# Yellow Flag: Hands-on scaling isn't scalable
aws cloudfront update-distribution --id E2EXAMPLE \
--default-cache-behavior "{ \
\"ManualScaling\": true \ # Requires human intervention
}"

This one setting added weeks to our integration plan. Manual processes during growth spurts? That’s like bringing a bicycle pump to fill a jumbo jet’s tires.

Real Costs of “Good Enough” Code

The $4.7 Million Sorting Mistake

A fintech firm boasted about their “optimized payment processing.” Their CTO assured us:

“We use industry-standard QuickSort across all transactions”

Our engineers discovered:

• Performance crashing with large datasets (O(n²) doesn’t scale)
• Currency rates frozen in 2018 – pre-pandemic economics anyone?
• Zero tests for transactions over $10k – an invitation for edge-case disasters

The fix cost millions and delayed their product roadmap by nearly a year. All from code that looked polished from 30,000 feet.

Your Tech Diligence Survival Kit

Next time you’re evaluating a target, ask these make-or-break questions:

• File structure sanity check: Can new engineers find features without a treasure map?
• Build process test: Do deployment scripts use containers, or rely on “Kevin’s magic laptop”?
• Load capacity proof: Does the system handle triple today’s traffic without coughing?

The Bottom Line: Disorder Compounds

Think of tech due diligence like preparing for a cross-country road trip. “Sort of sorted” code is like packing your map, snacks, and spare tire – but forgetting which box contains each. The journey becomes stressful, expensive, and full of unplanned stops. In acquisitions, messy code you tolerate today becomes your team’s emergency midnight fix tomorrow. Because in M&A, what’s hidden in the repositories always surfaces eventually.

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