An Effort Estimation Taxonomy for Agile Software Development

Abstract

In Agile Software Development (ASD) e®ort estimation plays an important role during release and iteration planning. The state of the art and practice on e®ort estimation in ASD have been recently identi¯ed. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on e®ort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on e®ort estimation in ASD. We applied a taxonomy design method to organize the identi¯ed knowledge as a taxonomy of e®ort estimation in ASD. The proposed taxonomy o®ers a faceted classi¯cation scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, e®ort predictors and e®ort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identi¯ed from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three di®erent software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions.

The Effects of Turnover on Expert Effort Estimation

“…Another area of the research is focused on the estimation error measurement [49] and the search for the reliable metrics, as well as on the comparison of different models [50], [51]. Besides simply relating the absolute values of estimated and actual effort, standardly used by the industry [52], researchers developed a number of more or less reliable measures of estimation error that indicate the accuracy of implemented models [53], [54]. De facto standard measures in this research area today are the magnitude of relative error with its derivatives, as well as indicator of the amount of correct predictions at set level, and they are described in more detail in Appendix C.…”

“…Calculation of the estimation error is performed based on the values of estimated (EST) and actual (ACT) effort. Besides the values of absolute and relative error typically used by the industry [65], there are a number of other scientifically accepted standard measures used to express the estimation error [53], [54]. In the study the most commonly used measures, the Mean Magnitude of Relative Error (MMRE), Mean Balance Relative Error (MBRE) and Prediction at level X (Pred(0.25)), are used.…”

Developing and using checklists to improve software effort estimation: A multi-case study

“…We organized the state of the art and practice on effort estimation in agile context as a facet based taxonomy [32], referred here as AET (Agile Estimation Taxonomy). At the top level, AET has four dimensions: context, estimation technique, effort predictors and effort estimate.…”

“…We used AET to characterize effort estimation processes of three companies. These companies found AET useful in documenting their effort estimation sessions [32]. The AET, however, was not used during the effort estimation process.…”

“…To select an appropriate statistical test, we applied normality tests (Shapiro-Wilk and Kolmogorov-Smirnov) in 252 SPSS 9 on BREbias values of estimates from all cases, which indicated that the tested data are not from a normally 3.5. Characterizing the three cases with the Agile Estimation Taxonomy (AET) [32] 257…”