The True Cost of Software Bugs

Move fast and fix things — the earlier a bug is caught, the cheaper it is to make it disappear

Apr 01, 2025

In 2022 Toyota had to halt all operations at all 14 of its Japanese factories, because of a single software bug in their supplier management system.

The estimated cost: $14 million per day in lost production.

"Could be worse" you could say, and you would be right: A unit conversion error in NASA's Mars Climate Orbiter led to a $327 million mission failure. In a separate incident, Knight Capital Group lost $440 million in 45 minutes due to a trading algorithm bug. Elsewhere, a coding error in Cloudflare's content delivery network briefly took down thousands of websites in 2019, affecting services from Discord to Shopify and impacting millions of users worldwide.

In 2020 alone, poor-quality software reportedly cost businesses $2.08 trillion globally according to the Consortium for Information & Software Quality (CISQ). That's not a typo—trillions of dollars evaporated because someone somewhere wrote a bug that didn't get caught until too late.

And these all happened before we needed a word for "vibe coding".

With more and more software being written by AI — is there anything that development teams can do to prevent bugs, and avoid their financial sinkholes?

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The 1:10:100 Rule of Software Quality

Taking the "1:10:100 rule" from manufacturing: if fixing a problem during the product’s design phase costs $1, then fixing the same bug during production will be $10, and the costs rise to $100 after the final object was delivered to the customer.

Software engineers have observed similar rules: according to research from IBM's System Science Institute, addressing defects after product release can be up to 100 times more expensive than fixing them during the design phase.

When developers have to fix a bug in production, they're pulled away from whatever they're currently working on. This context switching is incredibly costly in terms of productivity and momentum. Then there's the diagnostic complexity—finding bugs in production requires reconstructing complex user scenarios, often with limited information. Add to that the operational impacts like outages, security breaches, and data integrity issues, and you begin to see why the costs skyrocket. And we haven't even mentioned the reputation damage that can occur when customers encounter bugs—trust, once lost, can be incredibly difficult to regain.

Hard numbers

Back in 1981, the software engineering pioneer Barry Boehm published a seminal study showing that defects become exponentially more expensive to fix as they move through the development cycle. According to his research, a bug costing $100 to fix during coding would cost $1,500 during system testing and up to $10,000 post-release.

While the specific numbers have evolved over time, the fundamental principle—now sometimes called "Boehm's Law"—remains as valid in today's cloud-native world as it was in the era of mainframes. The longer a bug lives in your codebase, the more expensive it becomes to get rid of.

This idea of working quickly to keep costs to a minimum isn't just intuitive—it's supported by extensive research. A study conducted by the National Institute of Standard Technology (NIST) estimated that it takes three times as long for developers to fix a bug in the production stage as it did during the coding stage.

Move fast and fix things

Agile and other modern software development methodologies, and the rise of modern devops tooling & CI/CD helps with catching bugs early, and therefore flattening the cost curve.

Continuous Integration creates shorter feedback loops, allowing teams to catch integration issues within hours instead of weeks. Automated testing increases coverage and consistency, helping teams find more bugs earlier in the process. Feature flags let teams isolate new code in production, limiting the impact of defects. And modern monitoring and observability tools help teams identify abnormal behavior before users even report problems.

These practices don't eliminate the cost gradient—but they do pull bug discovery earlier in the cycle when fixes are less expensive. According to the 2019 State of DevOps Report, teams using best practices experience 24 times fewer failures and recover from incidents 44 times faster than their less mature counterparts. That's a competitive advantage that’s hard to ignore.

The art of triage

The reality is that in any moderately complex software project, you'll probably discover more bugs than you can immediately fix. This is where prioritization becomes crucial. Not all bugs are created equal—some you can live with for months without significant impact, while others require the entire team to drop everything and address them immediately. How do you make these decisions in a systematic way?

Many successful businesses build their prioritization system around two fundamental elements. First, they consider the investment: How much will it cost to fix the bug today compared to fixing it tomorrow, or next week? Sometimes, delaying a fix makes sense from a resource perspective. Other times, the "interest rate" on technical debt is so high that immediate action is the only financially responsible choice.

The second element of prioritization: impact on the business. Does the bug affect the customer experience, or can users still accomplish their goals despite it? Is it affecting core functionality or peripheral features? Does it impact all users or just a small segment? Is it causing data integrity issues that might compound over time?

This prioritization approach can vary based on your company's stage and context. A startup racing toward product-market fit might tolerate non-critical bugs that an established enterprise with millions of users would immediately address. A healthcare application will have different priorities than a mobile game. The key is having a consistent framework that aligns your bug-fixing efforts with your broader business objectives.

Effectively prioritizing bugs isn't just about deciding what to fix first—it's also about explicitly deciding what can wait or perhaps never needs fixing at all. Because in software development, as in life, you can do anything, but you can't do everything.

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