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Planning poker, also called Scrum poker, is a consensus-based, gamified technique for estimating, mostly used for timeboxing in Agile principles.In planning poker, members of the group make estimates by playing numbered cards face-down to the table, instead of speaking them aloud.
The Use Case Points method (UCP) Weighted Micro Function Points (WMFP) Wideband Delphi; Most cost software development estimation techniques involve estimating or measuring software size first and then applying some knowledge of historical of cost per unit of size. Software size is typically sized in SLOC, Function Point or Agile story points.
These values are used to calculate an E value for the estimate and a standard deviation (SD) as L-estimators, where: E = (a + 4m + b) / 6 SD = (b − a) / 6. E is a weighted average which takes into account both the most optimistic and most pessimistic estimates provided. SD measures the variability or uncertainty in the estimate.
The estimation approaches based on functionality-based size measures, e.g., function points, is also based on research conducted in the 1970s and 1980s, but are re-calibrated with modified size measures and different counting approaches, such as the use case points [11] or object points and COSMIC Function Points in the 1990s.
The Wideband Delphi estimation method is a consensus-based technique for estimating effort. [1] It derives from the Delphi method which was developed in the 1950-1960s at the RAND Corporation as a forecasting tool. It has since been adapted across many industries to estimate many kinds of tasks, ranging from statistical data collection results ...
While SLOC is an accepted way of measuring the absolute size of code from the developer's perspective, metrics such as function points capture software size functionally from the user's perspective. The function-based sizing (FBS) metric extends function points so that hidden parts of software such as complex algorithms can be sized more readily.
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More formally, it is the application of a point estimator to the data to obtain a point estimate. Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference. More generally, a point ...