3 Critical Code Quality Red Flags That Can Derail Your M&A Tech Due Diligence

Introduction

When one tech company looks to acquire another, a thorough technical review is essential. I want to share why a target company’s handling of code quality can make or break an M&A deal. Over my years advising on M&A tech due diligence, I’ve seen how deals succeed or stumble based on the health of a codebase, its scalability, and underlying risks. Think of it like inspecting a classic car before purchase—you need to look under the hood. In this post, I’ll walk you through three critical code quality red flags to watch for: maintainability issues, scalability gaps, and hidden technology risks. Let’s get started.

Why Technical Due Diligence Matters in M&A

Mergers and acquisitions come with big risks. Overlook technical weaknesses, and integration can fall apart after the deal closes. I’ve noticed acquirers sometimes fixate on financials and market share, while downplaying the tech review. But a company’s technology stack is its operational heart. Flaws here can shrink the deal’s value or even sink it completely.

The High Cost of Ignoring Code Quality

Imagine a target company with heavy technical debt—messy code, weak architecture, or outdated libraries. I once worked with a fast-growing startup that had strong revenues but a tangled, untested codebase. After acquisition, the buyer spent millions refactoring and faced product delays. That’s why a code quality audit isn’t just routine; it’s essential for avoiding future costs.

Scalability: The Make-or-Break Factor

Scalability problems often surface only under pressure, like a surge in users or post-merger demands. I remember a target whose system worked fine day-to-day but buckled under 10x the traffic. Without proactive scalability checks, acquirers can inherit platforms that can’t grow, leading to lost customers and a damaged reputation.

Conducting a Code Quality Audit

A solid code quality audit looks at how maintainable, efficient, and well-built the target’s code is. It also reveals the team’s engineering habits and discipline.

Key Metrics to Evaluate

Code Complexity: Tools like SonarQube or CodeClimate measure complexity and cognitive load. High complexity usually means more bugs and tougher maintenance.
Test Coverage: Look for at least 80% coverage in key areas. Low coverage raises the risk of hidden issues.
Documentation: Check for comments, API docs, and guides. Weak documentation can slow integration and confuse new teams.

Actionable Example: Analyzing a Sample Codebase

Suppose you’re reviewing a Python service for an e-commerce site. Here’s a code snippet that shows common problems:

def process_order(order_id):
# Nested loops and unclear error handling
for item in order.items:
if item.stock > 0:
for discount in item.discounts:
# Complex logic without comments
apply_discount(item, discount)
else:
raise Exception("Out of stock")
# Missing logging and transaction rollbacks

This code is hard to follow, fragile, and not built to scale. Improving error handling and moving to async processing could reduce these risks.

Assessing Scalability and Performance

Scalability checks ensure the target’s infrastructure can grow with demand. This includes load testing, reviewing architecture, and planning for capacity.

Load Testing in Practice

Use tools like Apache JMeter or k6 to mimic user traffic. Test APIs with many users at once and track response times. I usually flag systems if the p95 latency goes over 200ms at twice the current load.

Architecture Review

See if the system uses microservices or a monolith. Microservices scale well but add management complexity. Look for decoupled parts and smart database sharding. In one review, a monolith couldn’t handle growth, forcing a expensive rebuild after acquisition.

Technology Risk Analysis

Technology risk analysis finds vulnerabilities, from security gaps to shaky dependencies.

Security Vulnerabilities

Scan for known CVEs in libraries with tools like Snyk or OWASP Dependency-Check. For instance, a Java app using an unpatched log4j version is a serious concern.

Dependency Management

Check the state of third-party libraries. Old or unsupported dependencies bring security and compatibility risks. I suggest keeping a live inventory with tools like Dependabot or Renovate.

Conclusion

Technical due diligence is key to M&A success. By carefully reviewing code quality, testing scalability, and uncovering tech risks, you can dodge expensive surprises and ensure smoother integration. Never forget: a target’s technical condition is just as important as its financials. Use these tips in your next deal to spot red flags early and move forward with confidence.

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