76 Days Without a Single Code Change: What Software Stability Looks Like in Production

The Setup

Software in production breaks. It is one of the few universal truths of the technology industry. Code that works perfectly in testing fails under real-world conditions. Servers need patches. Dependencies get updated and introduce conflicts. User behavior triggers edge cases nobody anticipated. The result is that most production software requires ongoing maintenance -- someone watching it, fixing it, and deploying updates on a regular cadence.

This is why software businesses budget for maintenance as a recurring cost. A 2024 Gartner analysis found that enterprises spend 60-80% of their IT budgets on maintaining existing systems rather than building new ones. Stripe's Developer Coefficient study found that the average developer spends 42% of their time on maintenance and technical debt. The software industry operates on the assumption that code in production needs constant attention.

For business operators, this maintenance burden means ongoing cost and ongoing dependency. You either have a team watching your production systems or you have a vendor you are paying to keep things running. The idea that production software could run for an extended period without any intervention sounds too good to be true. But the question is specific: what does software stability actually look like when the architecture is built right?

What the Data Shows

The industry benchmarks for software stability paint a clear picture of the baseline. According to the Uptime Institute's 2024 Global Data Center Survey, the average unplanned outage rate for production systems is 60% of organizations experiencing at least one significant outage per year. A 2023 New Relic State of Software Engineering report found that the mean time between deployments for production systems is 4-7 days -- meaning most production software gets changed weekly.

PRJ-05, an insurance quoting platform in the South African market built by operator Michael George Keating, ran for 76 consecutive days in production (October 9 through December 22, 2025) without a single code change. No patches. No hotfixes. No emergency deployments. No maintenance commits. The platform processed leads, served 9 insurance verticals, and operated its LeadByte API routing -- all without intervention.

This was not a dormant site sitting idle. PRJ-05 is a production lead generation platform covering 9 insurance verticals in the South African market: car, life, medical, business, pet, legal, motor warranty, funeral cover, and vehicle tracker. It includes multi-step quote funnels, LeadByte API lead routing, an organic content hub with 10 SEO articles, product pages with provider cards, and CI/CD deployment infrastructure.

The full build metrics for PRJ-05:

Metric	Value
Total lines of code	16,993
Total files	84
Active development days	20
Total commits	97
Net-new delivery rate	73.2%
Rework rate	26.8%
Peak day	January 12 -- 22 commits
Production stability gap	76 days with zero code changes

The 76-day gap is the strongest stability proof of any system in the portfolio. The 26.8% rework rate during the active build period is higher than the insurance cluster products (which averaged 3.7-3.9%), but that rate reflects the fact that PRJ-05 was built with a smaller team (80.4% operator, 19.6% CON-01) and without the benefit of the mature shared architecture that the later products inherited. The stability of the finished product -- measured by 76 days of zero-intervention operation -- demonstrates that the final build was clean enough to run unattended.

The market replacement value for a 9-vertical insurance lead generation platform with content, SEO infrastructure, and API routing is estimated at $40,000-$80,000. The actual build cost (sweep allocation) was $303.

How It Works

The 76-day stability gap is not the result of simply building the product correctly. It is the result of two specific architectural decisions.

First, the platform was built with minimal external dependencies. Every external dependency is a potential failure point. When a third-party API changes its response format, your code breaks. When a library releases an update, your compatibility breaks. PRJ-05 minimized this surface area by keeping its dependency chain short and its integrations clean.

Second, the platform was tested in production through actual use before the stability period began. The 20 active development days included real-world testing and iteration. By the time the last commit was made on October 9, the code had been through production conditions. The 76-day gap that followed was not a gap in attention -- it was proof that the code was stable enough to not need attention.

For context, most production systems receive weekly deployments not because new features are being added, but because bugs are being fixed, dependencies are being updated, and infrastructure issues are being patched. A 76-day gap with zero changes means none of those maintenance triggers fired. The code did what it was supposed to do, without modification, for two and a half months.

What This Means for Business Operators

Production stability directly affects operating costs. Every maintenance deployment requires someone's time. Every emergency fix requires someone's attention. Every ongoing dependency on a developer or vendor to "keep things running" is a recurring cost that compounds over time.

A platform that runs for 76 days without intervention is a platform that costs nothing to maintain during that period. For operators evaluating build quality, the stability gap is the most practical metric available. It answers the question that matters: "Once this is built, will I need to keep paying someone to babysit it?" For PRJ-05, the answer was no -- for 76 consecutive days. That stability proof is worth more than any testing report or code quality score, because it was measured in production with real traffic, real data, and real operational conditions.

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Related: How Shared Software Architecture Delivered 3.7% Rework Across 4 Products | How to Expand a Digital Product to a New Country in 16 Days

References

1. Gartner (2024). "IT Budget Analysis." Enterprise spending on maintaining existing systems versus building new ones.
2. Stripe. "Developer Coefficient Study." Average developer time on maintenance and technical debt.
3. Uptime Institute (2024). "Global Data Center Survey." Unplanned outage rates for production systems.
4. New Relic (2023). "State of Software Engineering Report." Mean time between deployments for production systems.
5. Keating, M.G. (2026). "The Compounding Execution Method: Complete Technical Documentation." Stealth Labz. Browse papers