estimate-calibrator

To calculate PERT expected values for three-point effort estimates, you provide best, likely, and worst durations per task to compute expected totals, per-task standard deviations, and aggregate uncertainty via root-sum-square for confidence intervals.

The best way to estimate software features with unknowns is using calibrated three-point estimates that identify technical, scope, and external unknowns, document mitigations, and recommend buffers or spikes based on assigned confidence levels.

You aggregate uncertainty across multiple project tasks by computing PERT expected values and per-task standard deviations, then applying root-sum-square aggregation to produce overall project confidence intervals and transparent sizing ranges.

You need three-point estimates for infrastructure changes when single-point guesses hide uncertainty and risk causing missed deadlines, requiring structured best, likely, and worst durations with documented assumptions for stakeholder transparency.

Yes, you can use PERT estimation for migrating a monolith to microservices by decomposing the work into atomic tasks, capturing three-point durations, identifying integration unknowns, and calculating confidence rationale for stakeholders.

Project estimation misses deadlines because single-point guesses lack uncertainty ranges, whereas applying PERT aggregation with root-sum-square and historical calibration ratios surfaces realistic risk, identifies dominant unknowns, and recommends appropriate buffers.