One of the most dangerous assumptions we can make about production software is that code must be healthy because it has been working for years.
I was reminded of that recently when a stable, long-running client application suddenly became unable to communicate with its own APIs. The application had been in production in its current form for more than five years, while some of the underlying communication code had been running for more than a decade. It had been reliable, reused in multiple places, and had even survived a degree of load testing.
Then, one morning, it stopped working.
This incident became a valuable reminder: hidden technical debt is still technical debt. It may remain quiet for years, but quiet is not the same as safe.
A Stable Application - Until It Wasn't
The application used a content management system for its front end and communicated with a .NET Core back end through web APIs. Normal content requests continued to work, but requests requiring API communication began failing with errors indicating that a TCP connection could not be established.
We worked through the expected diagnostic questions:
- Had the firewall configuration changed? No.
- Were new virtual network rules blocking traffic? No.
- Were the APIs online and responding? Yes.>
- Were requests reaching the APIs and then failing? No, the requests never arrived
That last detail mattered. The APIs were healthy, but the application could not create the outbound connections needed to reach them.
The Real Problem: SNAT Port Exhaustion
Azure App Service diagnostic tools pointed us toward Source Network Address Translation, or SNAT, port usage. Each outbound connection needs access to a finite networking resource. If an application creates too many connections, fails to reuse them, or does not release them correctly, it can exhaust the available ports.
At approximately 9:06 a.m., the site received aggressive traffic from AI scraping bots targeting several APIs. Within about a minute, pending and failed outbound connection counts climbed together. The application had reached the point where it could no longer establish the connections required for API traffic.
The bot traffic was the trigger, but it was not the root cause. A healthy implementation should have handled the connections much more efficiently. The traffic simply exposed a weakness that had been present for years.
The Code Looked Reasonable at First
The affected code was legacy .NET Framework code built around HttpWebRequest. At a glance, much of it looked acceptable. The successful response path used disposal patterns around the response, response stream, and reader.
But a closer review revealed several problems:
- The request explicitly used HTTP/1.0.
- The request didn't request keepalive
- Connection reuse was not being handled as effectively as it could be.
- The exception path did not consistently dispose of the response and related resources.
That exception path was especially important. Under ordinary traffic, it might rarely execute. Under aggressive traffic, however, a few failures can produce more improperly managed resources, which lead to more connection pressure and still more failures. A small weakness can become a cascading production incident.
The block of code responsible had not been modified in roughly eight years. A modern code review would likely have found the problem, but unchanged code does not naturally re-enter the review process. That is exactly how technical debt becomes hidden.
A Small Fix with a Massive Result
We updated the implementation to follow better resource-management practices for .NET Framework 4.8:
- We stopped forcing HTTP/1.0 and returned to the appropriate default behavior.
- We enabled keep-alive behavior to improve connection reuse.
- We ensured that the response, response stream, and reader were correctly disposed on both successful and exception paths.
The code change itself was not large. The production impact was.
Before the fix, the application commonly consumed approximately 600 to 800 SNAT ports and sometimes reached roughly 1,100. After the change, it served the same traffic while using only 14. Average page response time also improved by approximately 40 percent.
This was not a major rewrite, platform migration, or expensive rearchitecture. It was a focused correction to code that had appeared reliable for years.
Working Software Can Still Carry Risk
Organizations often avoid touching older applications because they appear stable. That can be a reasonable short-term business decision, but it should not be confused with evidence that the code has no meaningful risk.
A decision that was common years ago may no longer align with current best practices. Traffic patterns change. Dependencies change. Hosting environments change. Bots and automated scrapers create load patterns that were never part of the original design. Even if the code remains exactly the same, the environment surrounding it does not.
The practical lesson is not that every legacy system needs to be rewritten. It is that mature systems still need deliberate review. The code most likely to contain hidden debt may be the code nobody has had a reason to inspect recently.
Using AI to Find Debt Before an Outage
AI-assisted review can help us revisit old code without waiting for a feature request to bring it back into view. In this case, diagnostic evidence gave the review important context: the application was exhausting outbound ports, and we suspected that connections were not being reused or released correctly.
With that context, AI could help evaluate the implementation against appropriate .NET Framework 4.8 practices. The important part is not simply asking AI whether the code “looks good.” It is combining production telemetry, architectural knowledge, and clearly defined engineering standards to ask targeted questions.
We can apply that same process more broadly:
- Does this code manage network and database resources correctly?
- Does this Entity Framework usage create avoidable performance risks?
- Are HTTP connections pooled and reused appropriately?
- Do exception paths clean up resources as reliably as successful paths?
- Are older implementation choices still appropriate for today's environment?
AI does not replace engineering judgment or production observability. It can, however, help us apply our standards consistently across code that might otherwise remain untouched.
Bring Hidden Debt into the Light
Technical debt does not disappear because users cannot see it or because it has not caused an outage yet. It remains part of the application's risk profile, waiting for the right combination of load, failures, or environmental change to expose it.
Review your stable systems. Look closely at resource management, exception paths, networking behavior, database access, and other areas where small mistakes can accumulate. Use telemetry to identify suspicious behavior, and use modern review tools to evaluate old assumptions.
Most importantly, do not wait for “working” software to stop working before deciding it deserves another look.
Watch the full episode of The Tech Vault: Hidden Tech Debt Is Still Tech Debt: A Real Production Outage to see the production scenario, diagnostic evidence, code changes, and results. Then share your own experience: What hidden technical debt have you encountered, how long did it remain unnoticed, and what finally exposed it?