How ‘Cherry Picking Fake Bin’ Findings Shape M&A Tech Due Diligence Outcomes

When one tech company acquires another, technical due diligence makes the difference between a smart investment and a costly headache. In my years as a consultant, I’ve learned that one overlooked detail can change everything: the “fake bin” problem. It’s often the deciding factor that turns an exciting acquisition into buyer’s remorse – or sets the stage for long-term success.

The Hidden Risks in ‘Fake Bin’ Code: A Due Diligence Perspective

In M&A deals, especially tech-focused ones, due diligence isn’t just about checking boxes. It’s about understanding what you’re really buying. Most investors look at revenue, market position, and team strength. But the codebase? That’s where the real story is.

I’ve seen it time and again: startups with dazzling demos, impressive traction, and a codebase hiding a “cherry-picked fake bin.” This is code created for quick wins – prototypes never meant to see production, hacks that solved immediate problems but created long-term ones, or borrowed components used without proper planning.

Think of it like buying a beautiful house with a DIY basement renovation. The paint looks great for a showing, but the foundation is shaky. That’s your “fake bin” code. When discovered during due diligence, it can trigger renegotiations, delay integrations, or even sink the entire deal.

What is a ‘Fake Bin’ in Software Development?

A “fake bin” is code that exists outside normal development standards. It’s the digital equivalent of a construction shortcut. Common examples:

• Prototypes turned into production code to hit demo deadlines
• Legacy modules nobody wants to touch
• Mock data and placeholder functions in “working” demos
• Third-party code dropped in without proper vetting

The problem? This code gets “cherry-picked” – showcased in presentations for its immediate value while ignoring its long-term consequences. I remember one founder proudly showing a seamless customer onboarding flow. Three weeks into due diligence, we found it was a single script cobbled together for investors, with no error handling, testing, or scalability.

Why ‘Fake Bin’ Code is a Red Flag During M&A

In my experience, “fake bin” code raises three major concerns:

1. Hidden Technical Debt

Every line of fake bin code is a future problem. It’s not just about old frameworks or messy syntax. It’s about what happens when you need to scale, fix, or integrate.

Last year, I reviewed a company that had built its core feature using custom JavaScript modules for a flashy demo. The buyer loved it. Post-acquisition, those same modules became a nightmare – they couldn’t work with the buyer’s microservices architecture. Rewriting them cost six figures and delayed the entire integration by four months.

2. Scalability and Integration Risks

Fake bin code is built for the moment, not the future. It usually fails at scale. When merged with an acquirer’s systems, it can cause:

• System slowdowns during peak loads
• Security gaps from missing error handling
• Update nightmares – one change breaks everything

Just last quarter, I found a target company using an obsolete database library. Simple for their small user base, but a ticking time bomb for enterprise-scale. Migration took months and required rewriting half their core functionality.

3. Licensing and Compliance Risks

Fake bin code often contains borrowed elements with messy origins. I once found a company using a GPL-licensed library in their proprietary product. Their quick solution became our legal headache:

“We discovered GPL code in their image processing module without proper attribution. Our options? Remove it and lose core functionality, or open-source their entire platform. Either choice had major financial consequences.”

Assessing the ‘Fake Bin’ During Due Diligence

When I evaluate “fake bin” code, I follow a three-step process:

1. Code Quality Audit

Start with the code itself:

• Static Analysis: Run tools like SonarQube. I look for complex, duplicated code with minimal tests. If cyclomatic complexity is high, alarm bells ring.
• Manual Review: Nothing beats experienced developers studying the code. I ask: Is it documented? Are there comments explaining the “why” behind the code? Do I see “TODO” notes or quick fixes?
• Dependency Check: Use Snyk or Dependabot to find outdated or risky libraries. Fake bin code often relies on deprecated tools.

Here’s how to quickly spot outdated dependencies:



npm audit --json | grep -E "(severity|vulnCount)" | sort | uniq -c


2. Scalability Assessment

Can this code grow with the business? I check:

• Load Testing: Use JMeter to simulate real-world traffic. Watch response times and error rates.
• Architecture: Is it modular? Can it be split into microservices? I look for stateless design and containerization potential.
• Database Health: Check query efficiency with EXPLAIN statements. Slow queries in a small dataset become disasters at scale.

Good scalable code is built for growth. An authentication service, for example, should use stateless JWT tokens instead of server-side sessions.

3. Technology Risk Analysis

Beyond the code, I evaluate broader impacts:

• Integration: How easy to connect with our systems? Are APIs compatible? Do data formats match?
• Maintenance: Who owns this code? Is documentation clear enough for new developers? If the original team leaves, can we maintain it?
• Legal: Verify licenses, IP ownership, and data compliance. No one wants a surprise GDPR violation.

One deal almost collapsed when we found a fake bin module using an API without a contract. The provider later changed pricing, increasing their costs by 300%.

When ‘Fake Bin’ Code Becomes a Green Light

Not all fake bin code is bad news. Sometimes it shows resourceful problem-solving. The key? How the company handles it.

1. Transparency and Documentation

Look for honesty. A company that admits their shortcuts and has a plan to fix them gets my respect:

• A clear roadmap for cleanup and refactoring
• Git history showing gradual improvements
• Someone clearly responsible for technical debt

2. Automated Testing and CI/CD

Fake code with tests is safer. Automated pipelines mean refactoring won’t break things. I want to see:

• Unit, integration, and end-to-end tests
• CI/CD tools in action (Jenkins, GitHub Actions)
• Code coverage above 70%

3. Scalability Roadmap

Companies planning for growth earn my trust. Questions I ask:

• Are they adopting containerization?
• Do they use cloud-native tools like AWS Lambda?
• Have they implemented monitoring (Prometheus, Grafana)?

Actionable Takeaways for M&A Due Diligence

From years of spotting these issues, here’s my checklist:

• Audit the code properly: Use automated tools, manual reviews, and dependency scanning. Surface-level checks miss the real story.
• Test scalability: Run load tests, study the architecture, check database performance.
• Assess integration challenges: Ask about API compatibility, data formats, and maintenance plans.
• Verify compliance: Confirm licensing, IP ownership, and data privacy measures.
• Look for a fix plan: A clear, documented path to clean up fake code is a good sign.

Conclusion

The “fake bin” code issue comes up in nearly every tech acquisition I handle. It’s not always a deal-breaker, but it’s always a conversation starter. I’ve seen companies negotiate price adjustments based on code quality, request escrow for future refactoring costs, or walk away entirely.

The best outcomes happen when the target company is upfront about their shortcuts. That honesty, combined with a clear plan to fix problems, tells me they’re serious about quality. After all, in tech M&A, the code is the product. If it’s built on shaky ground, everything else becomes questionable.

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