Software development effort estimation: Difference between revisions

Revision as of 13:10, 13 September 2020

Revision as of 15:08, 19 October 2020

Line 47:

}}</ref> and Nelson.<ref>Nelson, E. A. (1966). Management Handbook for the Estimation of Computer Programming Costs. AD-A648750, Systems Development Corp.</ref>

Most of the research has focused on the construction of formal software effort estimation models. The early models were typically based on [[regression analysis]] or mathematically derived from theories from other domains. Since then a high number of model building approaches have been evaluated, such as approaches founded on [[case-based reasoning]], classification and [[regression trees]], [[simulation]], [[neural networks]], [[Bayesian statistics]], [[lexical analysis]] of requirement specifications, [[genetic programming]], [[linear programming]], economic production models, [[soft computing]], [[fuzzy logic]] modeling, statistical [[bootstrapping]], and combinations of two or more of these models. The perhaps most common estimation methods today are the parametric estimation models [[COCOMO]], [[SEER-SEM]] <ref>{{cite web
| author = Fischman, McRitchie, Galorath
| title = Inside SEER-SEM
| url = https://apps.dtic.mil/sti/pdfs/ADA487403.pdf
}}</ref> and SLIM. They have their basis in estimation research conducted in the 1970s and 1980s and are since then updated with new calibration data, with the last major release being COCOMO II in the year 2000. 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|use case points]]<ref>{{cite journal

| author = Anda, B. Angelvik, E. Ribu, K.

| title = Improving Estimation Practices by Applying Use Case Models