Glue for Engineering Planning

By Arjun Mehta

Estimation is where planning succeeds or fails, and most teams struggle with it. Engineering estimation best practices call for breaking work into small units, using historical data, and accounting for unknowns. But these practices are difficult to follow when the people doing the estimating lack full visibility into the codebase. Glue changes this dynamic by generating file-level development plans from specs, giving teams concrete data to estimate against instead of relying on gut feel.

Bad estimates do not just misalign timelines. They erode trust between engineering and product. They cause scope cuts, crunch periods, and missed commitments. Improving estimation accuracy by even 20 percent has outsized impact on team health and business outcomes.

Engineering estimation fails for predictable reasons.

Hidden complexity. A feature that looks simple in a spec may touch deeply interconnected code. The developer estimating the work might not know about a legacy module that must be modified or a service dependency that constrains the approach. This hidden complexity surfaces during implementation, turning a two-day estimate into a two-week project.

Inconsistent knowledge. The engineer assigned to estimate may not be the one who builds it. And even the assigned builder may lack familiarity with every affected area. On a large team, no single engineer has complete knowledge of the entire codebase. Estimates reflect individual knowledge rather than system reality.

Abstract scope. Specs describe outcomes: "users should be able to filter dashboards by date range." Engineers must mentally translate this into code changes: which components, which APIs, which database queries, which tests. This translation happens in each engineer's head, leading to wide variance in estimates for the same spec. Sprint planning is broken precisely because this translation step is unstructured.

Anchoring bias. Once someone says a number, it anchors the room. Teams rarely adjust far from the first estimate offered, even when subsequent discussion reveals additional complexity.

Glue addresses these problems by generating a file-level development plan before estimation begins.

When a spec or feature description is provided, Glue analyzes the codebase to identify every file, module, and service that will likely need modification. It produces a structured plan that includes:

• Files to modify: A specific list of source files that will need changes, grouped by type (API, frontend, database, tests).
• New files to create: Where the spec requires new functionality that does not exist yet, Glue identifies what needs to be built and where it should live based on existing project structure.
• Dependencies affected: Other features and services that may be impacted by the planned changes. This dependency map prevents the surprises that derail mid-sprint execution.
• Risk flags: Areas where the planned changes interact with high-complexity or high-debt code. These flags signal where extra estimation buffer is warranted.
• Historical context: If similar changes have been made before, Glue references them. Knowing that "the last time we added a new filter type, it took three days and touched these seven files" is powerful calibration data for effort estimation.

This plan serves as the input for the estimation conversation rather than a vague spec. Instead of asking "how long will this take?" the team discusses a concrete list of changes and estimates each one.

File-level plans improve estimation in three ways.

Reduced unknown unknowns. The most dangerous estimation errors come from work that nobody anticipated. When the plan identifies affected files in advance, the scope of unknowns shrinks significantly. Teams can still encounter surprises, but the likelihood drops.

Calibrated discussion. When the estimation conversation references specific files, the engineer most familiar with each file can contribute precise input. "That component is straightforward, half a day" or "that module has a complex state machine, budget two days." The conversation shifts from abstract sizing to concrete assessment.

Better decomposition. Breaking work into file-level changes naturally produces smaller estimation units. Smaller units are easier to estimate accurately. The aggregate is more reliable than a single large estimate because errors in individual pieces tend to cancel out rather than compound.

Teams using Glue for planning report measurable improvements. Estimation accuracy typically improves by 25 to 40 percent within the first quarter. Sprint completion rates increase because plans account for the actual scope of changes rather than an optimistic subset.

Engineering leaders gain an additional benefit: visibility into the planning process. When plans are generated from code analysis rather than verbal discussion, they become reviewable artifacts. A VP of Engineering can look at a development plan and understand why a feature was estimated at three weeks rather than one, without needing to attend the planning meeting.

This transparency reduces the friction between "why is this taking so long?" conversations and the reality of software development. When the plan shows that a seemingly simple feature touches 40 files across four services, the timeline makes sense on its own.

How does Glue help with engineering planning? Glue analyzes your codebase against a feature spec to produce a file-level development plan. This plan lists every file to modify, new files to create, dependencies affected, and risk areas. Teams use it as the basis for estimation rather than estimating from the spec alone.

Can Glue generate dev plans? Yes. Glue generates structured development plans that include specific files, dependency maps, risk flags, and historical comparisons. These plans are starting points for engineering discussion, not prescriptive mandates. Teams refine them based on their own judgment.

How do file-level plans improve estimates? File-level plans improve estimates by making scope concrete. Instead of estimating an abstract feature, engineers estimate changes to specific files they know. This reduces unknown unknowns, enables the right people to contribute input on each component, and produces smaller estimation units that are individually more accurate.