How Undocumented Technical Debt Becomes the ‘Dateless SLQ’ in M&A Due Diligence

When Technical Debt Hides in Plain Sight

Imagine buying a rare coin only to discover its mint date has worn off. That’s exactly how undocumented technical debt feels during tech acquisitions. Over 12 years of conducting M&A technical due diligence, I’ve learned that what companies don’t document often matters more than their shiny metrics.

Why Code Quality Audits Make or Break Deals

The Story Your Comments Tell

We once reviewed a company with pristine documentation—until we noticed their JSDoc comments froze in 2018. The reality? Their payment system contained this jaw-dropper:

function processPayment(userId, amount) {
// TODO: Refactor this by Q3 2019 (jira PROJ-142)
const db = connectToLegacyDB();
const user = db.query('SELECT * FROM users WHERE id = ?', [userId]);
if (user.legacyCredit) {
applyDeprecatedDiscount(user, amount);
}
// ... 287 more lines
}

This single function revealed:

• TODOs gathering dust since the last presidency
• Direct handshakes with retired systems
• Discount logic only the original developer understood
• A complete lack of “what if this breaks?” planning

When Numbers Don’t Lie

Our analysis tools uncovered uncomfortable truths:

Critical Issues: 142 | Code Smells: 2,817 | Technical Debt Ratio: 38.2%

Yet their engineering lead insisted they were “98% modern.” Like authenticating a dateless Standing Liberty Quarter, we needed multiple verification methods to see the full picture.

Scalability Tests That Reveal Hidden Cracks

When Perfect Demos Fail Reality Checks

One $120M acquisition candidate demonstrated flawless scalability—until we simulated real traffic:

locust -f production_profile.py --users 5000 --spawn-rate 100

Their system crumbled at 2,300 requests per minute because of:

• Database bottlenecks from unsharded collections
• Cache systems overwhelmed during peak loads
• Hardcoded connections to ancient infrastructure

Just like examining wear patterns on coins, we learned more from their failure points than success metrics.

Temporary Solutions With Permanent Consequences

A fintech startup proudly showed us their microservices—except for the “temporary” monolith processing 84% of their revenue. The CEO promised post-acquisition fixes, but we found:

“ACHMonolith.java” last modified: 2019-04-22
Dependencies: JDK 8, Spring Boot 1.5.22 (EOL), OpenJPA 2.4 (vulnerable)

This relic contained 19% of their technical debt. Like a dateless SLQ, its true risk wasn’t immediately visible.

Uncovering Buried Risks in Tech Stacks

Dependency Time Bombs

Our forensic approach recently exposed:

• JavaScript packages abandoned for 3+ years
• Base container images with 43 known security holes
• Production servers running decade-old encryption

When the CTO argued “if it works, don’t touch it,” we showed the math:

Update Cost: $240k
Potential Breach Cost: $4.2M+

The Human Single Points of Failure

Technical due diligence isn’t complete without assessing team risks. We once discovered:

• A lone developer maintaining most critical systems
• Zero documentation beyond their memory
• No succession plan for their institutional knowledge

This discovery—like finding a rare coin variety—lowered the company’s valuation by 18%.

Real Deal Drama: The $200M AI That Wasn’t

A MarTech company dazzled buyers with:

• Near-perfect test coverage
• Impeccable uptime stats
• Polished deployment pipelines

Then we opened their “AI-powered” recommendation engine:

def recommend(user):
# Temporary until ML team delivers
return random.choice(POPULAR_PRODUCTS)

This “temporary” solution had been recommending products at random for three years. The deal survived—but with $47M held in escrow against performance claims.

What Good Looks Like

These practices make us breathe easier:

• Debt Footnotes: Code comments linking to active improvement tickets
• Documentation explaining why systems exist, not just how they work
• Preserved historical environments showing evolution

One company won us over with their “Legacy Museum”—a working replica of every architecture version since 2015. It demonstrated the kind of transparency that builds buyer confidence.

Practical Frameworks for Buyers and Sellers

Technical Debt Matrix

We map issues based on:

User Impact (Minor → Critical)
Visibility (Well-Documented → Buried)

High-impact hidden issues become deal-breakers, while documented manageable debt often just means financial adjustments.

Must-Ask Questions

Always verify:

• Can new engineers deploy without tribal knowledge?
• Are there production systems missing from infrastructure records?
• Do monitoring tools actually reflect user experience?

Don’t Let Your Tech Become a Mystery Coin

Undocumented technical debt during M&A due diligence works like coin wear—it erodes clarity about true value. Through rigorous code reviews, realistic load testing, and dependency analysis, we help separate valuable acquisitions from shiny traps. Treat your codebase like rare numismatic treasure: document its history, maintain its condition, and be honest about its flaws. Your acquisition partners—and future self—will thank you.

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