By Arjun Mehta, Principal Engineer at Glue
I spent three years as the principal engineer responsible for technical debt reduction on a mid-size SaaS platform. Every quarter, we ran a codebase health report. Every quarter, we created 15–20 Jira tickets from the findings. Every quarter, we closed most of those tickets. And every quarter, when we ran the next report, the numbers were almost exactly the same.
We were doing work. The tickets said so. Jira said so. Our velocity metrics said so. The codebase did not agree.
The Fundamental Problem with Ticket Systems
Ticket systems — Jira, Linear, GitHub Issues, Asana — are designed to track the state of work. They answer: "What is being worked on? Who owns it? Is it done?" These are coordination questions, and ticket systems answer them well.
What ticket systems cannot answer is: "Did the work actually address the underlying problem?" This seems like an obvious gap, but it is one that almost every engineering team has learned to overlook, because there is no immediate consequence to ignoring it. Tickets move to Done. Sprints close. Velocity stays stable. The codebase quietly accumulates the debt that the closed tickets were supposed to eliminate.
The disconnect happens because ticket systems have no connection to the codebase. They exist in a parallel universe of intentions and activities. When an engineer closes a ticket titled "Refactor auth service for maintainability," Jira has absolutely no way to verify whether the auth service is now more maintainable. It just knows the ticket is closed.
What Gets Lost Between Detection and Resolution
The journey from "we identified a problem" to "that problem is fixed" has multiple failure points that ticket systems are blind to.
Context collapse. A codebase analysis identifies that a specific file has 12 different authors, high churn rate, and declining test coverage — a clear risk signal. That finding gets summarized into a ticket title: "Tech debt in payment module." The engineer who picks up that ticket three sprints later has lost the original context. They might fix one of those three signals and close the ticket, leaving the other two untouched.
Scope drift. The work done in a sprint is rarely identical to what was originally specified. Engineers encounter unexpected complexity, discover related problems, or make pragmatic tradeoffs. The ticket closes, but what actually changed in the codebase might be significantly different from what the ticket described.
Problem recurrence. Even when a problem is genuinely fixed, the same conditions that created it often recreate it within months. A ticket system has no way to track whether a "fixed" problem has quietly reappeared. The next codebase health report will find it again, create a new ticket, and the cycle restarts.
According to the PMI, 70% of project failures trace to requirements problems — specs and tickets written without adequate understanding of the underlying system. The gap between what is written in a ticket and what the codebase actually requires is a primary driver of rework and cost overrun.
The Missing Layer: Outcome Verification
What engineering teams need — and what almost none of them have — is a systematic way to verify outcomes against the original codebase signals that created the work.
After a sprint closes, something needs to look at the codebase and ask whether the problem that prompted the ticket still exists. Not "was the ticket closed?" but "did the underlying condition change?" These are different questions, and ticket systems can only answer the first one.
Some teams try to close this gap manually: at the end of each sprint, an engineering manager re-runs the health report and cross-references it against closed tickets. This works, sort of, but it is time-consuming and error-prone. It also requires the original detection signal to be precise enough to be measurable — which "tech debt in payment module" is not.
The more systematic approach is closed-loop engineering intelligence: a platform that connects detection to work items to verified resolution automatically. When detection is specific — this file, this metric, this threshold — connecting it to a ticket and then verifying the outcome against the same metric becomes tractable.
What Happens When You Close the Loop
Teams that implement outcome verification consistently report the same finding: a significant fraction of their "completed" technical debt work was not actually completing anything. Not because engineers were not trying, but because the connection between the detected problem and the defined work was too loose to guarantee alignment.
When Glue connects codebase analysis to sprint work, teams can see in the post-sprint report which tickets corresponded to actual codebase improvements, which partially addressed the flagged issues, and which closed without measurable codebase change. This visibility alone changes how teams write tickets and how engineers scope their work — because the outcome is now visible, not just the activity.
The result is that technical debt work starts to actually reduce technical debt. For most teams running quarterly health reports that look identical quarter after quarter, this represents a fundamental change in how engineering investment is measured. See how it works in practice in Glue for Technical Debt Management.
Starting Without Ripping Out Your Ticket System
The goal is not to replace Jira. Jira is good at what it does. The goal is to add the layer that Jira is missing: a connection between ticket work and codebase outcomes.
Start small: pick one category of technical debt, define a specific measurable codebase metric, create tickets with that metric explicitly stated, and verify the metric after the sprint. Manual at first. Automated as the tooling matures.
What you are building is an engineering feedback loop that actually closes — where "done" means the codebase changed, not just the ticket state. For teams that have been running the same technical debt report for years and watching the numbers barely move, this is the intervention that makes the work matter.
FAQ
Why don't ticket systems track codebase outcomes?
Ticket systems were designed for work coordination, not codebase analysis. They track human workflow states — To Do, In Progress, Done — rather than technical outcomes in code. Connecting the two requires integrating a codebase intelligence layer with the work management system, something most teams have not done.
How can I tell if my technical debt tickets are actually working?
Run a codebase health metric before and after each sprint that includes debt work. Compare the specific metrics that were flagged — test coverage, churn rate, author concentration — against their post-sprint values. If the metrics have not moved, the ticket closed but the problem did not. Most teams discover that 30–50% of their debt tickets fall into this category.
What should a technical debt ticket include to be verifiable?
A verifiable debt ticket should include the specific file or module affected, the metric being targeted (for example, "test coverage currently at 23%, target 60%"), and a definition of done that can be measured in code, not just in activity. Tickets that describe work instead of outcomes are almost impossible to verify after the fact.