Use Case
See which sprints actually improved the codebase and which just closed tickets. Measure the real ROI of engineering effort.
By Priya Shankar, Head of Product at Glue
Picture this: you are in sprint planning. An engineering manager presents 12 "technical items" for the sprint. You nod along. You do not know what they are, what prompted them, or whether they matter for anything on the product roadmap. After the sprint, engineering reports 11 of 12 completed. You nod again. Was the sprint a success? You have no idea.
This is the intelligence gap in sprint planning. Product managers and engineering managers make sprint decisions from different information sets — product from roadmap and user feedback, engineering from codebase health reports. The connection between those two sets is usually a Slack message and a gut feeling.
Sprint planning happens in a work management system. Codebase intelligence happens in a separate analysis tool. The two systems do not communicate, and the people who use them are often in different rooms with different context.
For a product manager, this creates a specific problem: you cannot evaluate the urgency or business impact of engineering requests without understanding what codebase conditions prompted them. "Refactor the payment service" is uninterpretable without knowing whether it is flagged because of declining test coverage on a critical path, a single-owner risk on a module touching your highest-revenue feature, or a routine cleanup.
For an engineering manager, the problem is symmetric: you cannot connect technical investment to business outcomes without knowing how the codebase work relates to the product roadmap. "We spent two sprints on debt reduction" does not translate to "we reduced the risk on the payment module by 60%, which directly affects the Q3 checkout redesign."
95% of product managers cannot read code (airfocus/Gitnux, 2024). But they do not need to read code — they need context. And that context is sitting in the codebase intelligence that engineering already has, untranslated and unshared.
Glue maintains a persistent connection between codebase intelligence signals and sprint work items, so both product and engineering can see the same information in terms they understand.
Step 1: Glue surfaces sprint-relevant intelligence in product language. Before sprint planning, Glue shows engineering managers and PMs which codebase issues are relevant to the upcoming sprint's product goals. "The payment module has 31% test coverage. Three Q3 roadmap items touch this module. Before those items can be delivered predictably, coverage needs to reach 60%." This connects technical work to the roadmap context the PM cares about.
Step 2: Sprint tickets carry intelligence context. When Glue creates or enriches sprint tickets with codebase intelligence, the context travels with the ticket. An engineer picking up the ticket sees: what was detected, why it matters to the product roadmap, and what done looks like in metric terms. A PM reviewing the sprint board sees: this technical ticket is unblocking the checkout redesign in Q3, not just "tech debt."
Step 3: Post-sprint verification closes the loop for both sides. After the sprint, Glue verifies whether the flagged codebase condition improved. For engineering: did the metric move? For product: is the Q3 roadmap item now lower risk? The report connects technical outcomes to product context — which is the conversation engineering managers have been trying to have with leadership for years.
Product managers who see sprint intelligence in context consistently report two changes: they stop treating technical sprints as black boxes they cannot evaluate, and they start actively prioritizing technical work that is on the critical path of roadmap items they own. This is the alignment every engineering manager has wanted — not just engineering advocating for debt work, but product managers who understand why specific debt work matters for specific roadmap outcomes.
Engineering managers report the symmetric benefit: technical work gets prioritized more effectively when its business context is visible in sprint planning, and post-sprint reporting becomes more convincing when it shows verified codebase outcomes tied to product goals.
Glue connects to your Git repository and your roadmap context to surface sprint-relevant intelligence before planning and verify outcomes after each sprint. Try Glue free and see the intelligence behind your next sprint before it starts.
See also: Glue for Product Managers and Track Technical Debt From Detection to Verified Resolution.
It gives product managers the context to evaluate and prioritize technical sprint work — not by reading code, but by understanding which codebase conditions affect which roadmap items. When a PM can see that a technical sprint item directly unblocks a Q3 roadmap commitment, they can advocate for it in sprint planning instead of treating it as an opaque engineering request.
No. Glue adds intelligence context to your existing sprint workflow rather than replacing it. Sprint planning still happens in Jira or Linear. The difference is that technical items arrive with codebase context attached — what was detected, why it matters, and how it connects to product goals — rather than as bare ticket titles that PMs cannot evaluate.
The sprint intelligence loop is: codebase intelligence flags an issue before the sprint, the issue is connected to a sprint ticket with its context preserved, engineers do the work, Glue verifies the outcome after the sprint, and the verified outcome feeds back into the next sprint's intelligence. Each sprint makes the intelligence more accurate and the work more targeted.
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