Use Case
Track technical debt from detection to verified resolution. See exactly where your debt comes from and whether remediation actually works.
By Arjun Mehta, Principal Engineer at Glue
Most technical debt management stops at detection. Teams run a codebase health report, create tickets, do sprint work, and close the tickets — never confirming whether the underlying codebase condition actually changed. Glue closes this loop: from detecting specific debt signals to verifying, after sprint work completes, that those signals resolved.
Engineering teams face a consistent pattern with technical debt: work happens, but debt persists. Sprints close with completed tickets, yet the quarterly health report looks nearly identical to the previous one. The problem is not effort — engineers are spending 23–42% of their time on debt (Jellyfish, 2025). The problem is that the work is not systematically connected to the original detection signals, so there is no way to confirm resolution.
Without verification, debt management becomes a reporting exercise. Teams measure activity (tickets closed, sprints completed) instead of outcomes (debt reduced, codebase health improved). Leadership cannot tell whether the engineering investment is working. Engineers cannot tell whether their work is making a difference. The same files show up in every quarterly health report.
The specific failure is the gap between the work management system and the codebase. Jira knows whether tickets are closed; it does not know whether the codebase condition that prompted the ticket has changed. Codebase analysis tools know what is wrong; they do not know what work was done to address it or whether that work succeeded.
Glue connects detection, work, and verification in a single workflow.
Step 1: Glue detects and prioritizes debt with specific signals. At the start of each sprint, Glue analyzes your codebase and surfaces debt items with specific, measurable signals: "auth-service.js — test coverage 29% (critical path), 1 author (80% of commits in 6 months), cyclomatic complexity 44." Each item includes the specific metric value, the threshold it violates, and the business context — which features depend on this module.
Step 2: Signals are translated into verifiable work items. Glue creates sprint-ready work items that preserve the original detection signal: metric, current value, target value, affected files. An engineer picking up the ticket knows exactly what "done" means — not "refactor the auth service" but "bring test coverage from 29% to 60% in auth-service.js, session-handler.js, and token-validator.js."
Step 3: Work completes in your existing sprint workflow. Glue does not replace your sprint planning or ticket management. Engineers work in Jira or Linear as usual. Glue stays connected to the Git repository, tracking commits that touch the flagged files.
Step 4: Glue verifies resolution after the sprint closes. When the sprint ends, Glue re-analyzes the specific files and metrics from Step 1. For each debt item that had work done against it, Glue reports: the original metric value, the post-sprint metric value, and the verification status — Resolved (metric cleared the threshold), Partially Resolved (metric improved but below threshold), or Unchanged (no detectable codebase change).
Teams using Glue's full detection-to-verification workflow see two immediate changes. First, the verified resolution rate — the percentage of debt work that produces confirmed codebase improvement — becomes visible for the first time. Most teams discover it is lower than expected, which creates immediate motivation to write better tickets and scope work more precisely.
Second, debt actually starts declining, because work is connected to outcomes rather than just activity. Over a quarter, teams can show leadership a verified debt reduction number — not "we closed 23 debt tickets" but "23 of 31 flagged issues are confirmed resolved, with an average metric improvement of 58%." This transforms the engineering investment narrative from activity reporting to outcome accountability.
Glue connects to your Git repository (GitHub, GitLab, or Bitbucket) and starts detecting codebase health signals immediately. Try Glue free and run your first verified debt analysis within a day.
See also: Technical Debt Tracking: From Detection to Resolution and the Glue for Technical Debt Management use case.
Glue maintains the connection between detected codebase signals and the specific files they affect. When commits touch those files during a sprint, Glue tracks the changes and re-measures the original signals after the sprint closes. This does not require engineers to tag commits or update tickets — the connection is maintained through the file-level specificity of the detection.
Glue surfaces partially resolved items with the updated metric values and the remaining gap. These items automatically appear in the next sprint's debt queue with the updated baseline — so engineers know exactly where the previous work left off and what the remaining target is. Partial resolution is tracked across sprints until the item is fully resolved or deliberately deprioritized.
Yes. Glue's detected debt items can be pushed to Jira as tickets with the full signal context preserved. When those tickets close in Jira, Glue's verification runs against the codebase to confirm resolution. The result appears both in Glue's dashboard and alongside the Jira ticket, giving engineering managers verified outcome data within their existing workflow.
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