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Cost estimate; Delphi method; Documenting estimation results; Educated assumptions; Estimating each task; Examining historical data; Identifying dependencies; Parametric estimating; Risk assessment; Structured planning; Popular estimation processes for software projects include: Cocomo; Cosysmo; Event chain methodology; Function points ...
Cost estimation models are mathematical algorithms or parametric equations used to estimate the costs of a product or project. The results of the models are typically necessary to obtain approval to proceed, and are factored into business plans, budgets, and other financial planning and tracking mechanisms.
WBS-based (bottom up) estimation Expert estimation Project management software, company specific activity templates Parametric models Formal estimation model COCOMO, SLIM, SEER-SEM, TruePlanning for Software: Size-based estimation models [15] Formal estimation model
Bottom Up estimating: Using the lowest level of work package detail and summarizing the cost associated with it. Then rolling it up to a higher level aimed and calculating the entire cost of the project. Parametric Estimating: Measuring the statistical relationship between historical data and other variable or flow.
A parametric model is a set of related mathematical equations that incorporates variable parameters. A scenario is defined by selecting a value for each parameter. Software project managers use software parametric models and parametric estimation tools to estimate their projects' duration, staffing and cost.
Cost estimation in software engineering is typically concerned with the financial spend on the effort to develop and test the software, this can also include requirements review, maintenance, training, managing and buying extra equipment, servers and software. Many methods have been developed for estimating software costs for a given project.
One of the distinguishing features of the Putnam model is that total effort decreases as the time to complete the project is extended. This is normally represented in other parametric models with a schedule relaxation parameter. This estimating method is fairly sensitive to uncertainty in both size and process productivity.
In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables' density functions, or in kernel regression to estimate the conditional expectation of a random variable.