complexity-estimation

Estimate software task complexity using T-shirt sizing (XS to XL) by scoring seven factors: files touched, new concepts, integration points, data changes, testing complexity, regression risk, and uncertainty. This structured framework sets realistic expectations and identifies risks at the start of development.

Evaluate files touched, new concepts introduced, integration points, data schema changes, testing complexity, regression risk, and uncertainty levels. Additionally assess technical, integration, data, timeline, and security risks to determine if a spike or proof-of-concept is needed before committing resources.

A spike is needed when estimation reveals high uncertainty, novel technical challenges, complex integrations, or significant data architecture changes. Spike indicators emerge from the scoring process and help reduce risk before full feature development begins.

Yes. T-shirt sizing applies to authentication features like OAuth. Evaluate the integration points with your identity provider, new OAuth concepts, files to modify, data schema changes for user tokens, testing scope, and regression risks across your platform to reach an accurate size estimate.

T-shirt sizing provides standardized scoring across defined factors (files, concepts, integrations, data, testing, regression, uncertainty) and explicit risk categories, reducing estimation variability. It surfaces spike needs and testing complexity upfront, unlike simpler story-point or time-based approaches that lack structural risk assessment.

Apply complexity estimation at feature kickoff using the seven-factor scoring model. High scores in files touched, integration points, or data changes flag scope complexity; high uncertainty or regression risk indicate where additional resources or timeline buffers are needed to prevent delays and misallocation.