Glue for Technical Debt Management

By Arjun Mehta

Engineering teams know technical debt exists. They feel it in every sprint that takes longer than it should. But most organizations lack technical debt measurement tools that make debt visible, quantifiable, and trackable over time. Glue fills this gap by analyzing your codebase to surface debt, assign severity, and present it through dashboards that both engineers and business leaders can understand.

Technical debt is not inherently bad. Shortcuts taken deliberately to hit a launch date are rational decisions. The problem emerges when debt accumulates invisibly, when nobody tracks it, and when the compounding cost catches leadership by surprise. Measurement is the first step toward management.

Most technical debt is invisible to everyone except the engineers working in the affected code. And even they may only perceive it as "this module is hard to work in" without quantifying the impact.

This invisibility creates a cascading set of problems.

Product managers cannot factor debt into planning because they do not know where it exists or how severe it is. Engineering leaders struggle to justify debt reduction work to stakeholders because they lack data. Leadership approves feature work over maintenance because features have visible business cases and debt does not.

The result is a slow degradation. Velocity drops quarter over quarter. Bug rates climb. Onboarding time for new engineers increases. Everyone senses something is wrong, but nobody can point to specific numbers.

Traditional approaches to debt tracking rely on engineers manually tagging issues in Jira or maintaining a debt register. These methods depend on discipline and consistency, two things that erode under delivery pressure. When the sprint gets tight, updating the debt register is the first thing that gets skipped.

What teams need is automated detection. If debt measurement is a byproduct of normal development rather than extra work, it stays current. That is what Glue provides. For more context on why this matters, see our piece on technical debt visibility.

Glue analyzes your codebase along multiple dimensions to identify and categorize technical debt.

Code complexity analysis. Glue measures cyclomatic complexity, nesting depth, function length, and file size across your codebase. It flags hotspots where complexity has grown beyond maintainable thresholds. These hotspots are the areas most likely to slow development and harbor bugs.

Dependency health. Glue tracks your dependency tree, identifying outdated packages, known vulnerabilities, and libraries that have been deprecated or abandoned. Dependency debt is often invisible until a security audit forces an emergency upgrade.

Architectural drift. Glue compares your codebase's actual architecture against its intended structure. When modules that should be independent develop tight coupling, or when data flows through unexpected paths, Glue identifies the deviation. This type of debt is the most expensive to address and the hardest to detect manually.

Code duplication. Glue finds duplicated logic across your codebase. Duplication is a straightforward debt indicator: every duplicate is a place where a bug fix or feature change must be applied multiple times, increasing the risk of inconsistency.

Test coverage gaps. Glue maps which features and code paths have test coverage and which do not. Untested code is a form of debt because it increases the risk of regressions and slows the team's confidence in making changes.

For each debt item, Glue assigns a severity rating based on the code's change frequency, its centrality to the product, and the estimated cost of remediation. Debt in a rarely touched utility module ranks lower than debt in a core business logic file that gets modified every sprint.

Glue presents technical debt through dashboards designed for different audiences.

The engineering dashboard shows debt density by module, trend lines over time, and a prioritized list of remediation opportunities. Engineers can drill into specific files and see exactly what Glue flagged and why.

The leadership dashboard translates debt into business impact. It shows the correlation between debt-heavy modules and delivery slowdowns. It presents debt in terms of estimated velocity cost: "this module's complexity is adding an estimated X hours per sprint in additional development time."

The planning dashboard helps product and engineering leads incorporate debt reduction into sprint and quarterly planning. It shows which debt items deliver the highest return on remediation investment and suggests a sustainable pace for debt reduction.

Key metrics Glue tracks include:

• Debt ratio: the proportion of codebase flagged as carrying significant debt
• Debt velocity: whether debt is accumulating or being reduced over time
• Remediation ROI: estimated velocity recovery from addressing specific debt items
• Risk score: a composite score reflecting the likelihood of debt causing production issues

These metrics transform technical debt from a vague concern into a managed portfolio. Teams can set targets, track progress, and demonstrate improvement to stakeholders with concrete data.

How does Glue surface technical debt? Glue analyzes your codebase across five dimensions: code complexity, dependency health, architectural drift, code duplication, and test coverage gaps. It assigns severity ratings based on change frequency and business centrality, then presents findings through role-appropriate dashboards.

Can non-technical people see tech debt in Glue? Yes. Glue's leadership dashboard translates debt into business terms like velocity impact and risk scores. Non-technical stakeholders can understand the scope and trend of technical debt without reading code.

What debt metrics does Glue track? Glue tracks debt ratio, debt velocity (accumulating or reducing), remediation ROI (estimated velocity recovery), and risk score (likelihood of production impact). These are available as trend lines over time and can be filtered by module, team, or severity.