By Vaibhav Verma, Founder & CEO of Glue
Jira is one of the most successful software products ever built. It solves a real coordination problem extremely well: tracking the state of work across a distributed engineering team. This is genuinely hard, and Jira does it better than almost anything else at scale. I use it. Most companies I know use it.
It also has a structural limitation that almost nobody talks about: Jira has no connection to the codebase. It cannot tell you whether a technical problem was actually resolved. It can only tell you whether the ticket associated with that problem was closed.
The Difference Between Activity and Outcomes
When a sprint closes and the board shows 42 tickets in Done, the natural interpretation is that 42 things got done. And in the sense that 42 work items were completed, that is accurate. But "42 things got done" is an activity metric, not an outcome metric. The outcome question — did any of those 42 things resolve the technical problems they were created to address? — is one that Jira cannot answer.
This is not a criticism of Jira. It is a description of what project management tools are designed to do. They track coordination: who is working on what, in which sprint, and whether it is complete. Connecting that coordination to technical outcomes in the codebase would require Jira to read code, analyze metrics, and compare pre- and post-work states of specific modules. That is not project management. That is codebase intelligence.
The problem is that most engineering teams treat Jira's activity data as if it were outcome data. "We completed 15 technical debt tickets this quarter" is presented to leadership as if it means "technical debt declined this quarter." It might. It also might not. Jellyfish's 2025 research finds that engineers spend 23–42% of their time on technical debt — if even a fraction of that work is not producing verified outcomes, the waste is substantial.
Three Ways Ticket Closure Diverges from Problem Resolution
Understanding how these diverge explains why the gap is so persistent.
The ticket scope does not match the problem scope. A detected codebase problem might span 12 files. The ticket gets scoped to 3 of them because that is what fits in the sprint. The ticket closes as Done. The 9 remaining files still have the original issue. The ticket metric says success. The codebase says partial completion.
The fix addresses the symptom, not the cause. A high-churn file with multiple owners and declining test coverage might be showing those signals because of an upstream architectural issue — another module that forces repeated changes to the downstream file. Fixing the file directly reduces the symptoms temporarily. The architectural issue regenerates them within two or three sprints. Jira will happily track this cycle indefinitely.
The ticket gets closed for the wrong reason. Sprint pressure is real. Engineers close tickets when they have done what they can in the time available, even if the original problem is not fully resolved. This is not dishonesty — it is a rational response to the incentives. Closing a ticket signals progress. Leaving it open signals a problem. Without outcome verification, the rational move is always to close.
What Engineering Leaders Actually Need
Engineering leaders need two things that ticket systems do not provide: signal-connected work items and outcome verification.
Signal-connected work items mean that tickets are created with the specific codebase metric that triggered them, not just a description of work. "Auth service test coverage is 31% — target 70%" is a signal-connected ticket. "Improve auth service test coverage" is not. The former is verifiable. The latter is not.
Outcome verification means that after sprint work is done, someone or something re-measures the original signal and confirms whether it moved. This is what closes the engineering feedback loop. Without it, engineering leaders are managing activity, not outcomes — and activity management produces reports that look good but do not improve the codebase.
How to Use Jira and a Codebase Intelligence Layer Together
The answer is not to abandon Jira. Jira's coordination capabilities are valuable and hard to replicate. The answer is to use Jira for what it is good at — work coordination — and add a codebase intelligence layer for what it is not: detection, signal translation, and outcome verification.
In practice, this means: a codebase intelligence platform detects a problem and produces a specific, metric-backed finding. That finding becomes a Jira ticket with the metric attached. The sprint team does the work and closes the ticket. The intelligence platform re-reads the codebase and confirms whether the metric changed. The result — verified resolved, partially resolved, or unchanged — is surfaced to the engineering manager alongside the closed ticket.
This is how closed-loop engineering intelligence works. Glue connects to your codebase and your sprint workflow so that the technical debt work your team does in Jira is systematically verified against actual codebase outcomes, not just ticket status. The goal is not to replace Jira — it is to give Jira's activity data the codebase context it is currently missing. Learn more in the Glue vs Jira comparison.
FAQ
Why can't Jira just integrate with code analysis tools to verify outcomes?
The integration exists at the surface level — Jira can display code coverage numbers or static analysis results. What does not exist is the semantic connection: Jira does not know which ticket was supposed to improve which metric in which files. Without that connection, re-measuring after a sprint does not tell you whether the metric changed because of the ticket work or for other reasons.
How do I convince my team to track outcomes instead of just closing tickets?
Show them the data from one sprint: pick three closed technical debt tickets and re-measure the original codebase metrics manually. Report what you find at the retro. Teams that see the divergence between ticket closure and actual codebase improvement usually become self-motivated to change — not because they are told to, but because the gap is genuinely surprising.
What is the most common reason technical debt tickets do not fully resolve the problem?
Scope mismatch: the ticket was scoped to visible symptoms rather than the root cause, or to a subset of affected files rather than the full extent of the problem. Signal-connected tickets — those that specify exact metrics and files — have significantly higher verified resolution rates because engineers know precisely what "done" means.