Over the last 25+ years, the software community has been searching for the best models for estimating variables of interest (e.g., cost, defects, and fault proneness). However, little research has been done to improve the reliability of the estimates. Over the last decades, scope error and error analysis have been substantially ignored by the community. This work attempts to fill this gap in the research and enhance a common understanding within the community. Results provided in this study can eventually be used to support human judgment-based techniques and be an addition to the portfolio. The novelty of this work is that, we provide a way of detecting and handling the scope error arising from estimation models. The answer whether or not scope error will occur is a pre-condition to safe use of an estimation model. We also provide a handy procedure for dealing with outliers as to whether or not to include them in the training set for building a new version of the estimation model. The majority of the work is empirically based, applying computational intelligence techniques to some COCOMO model variations with respect to a publicly available cost estimation data set in the PROMISE repository.
Sarcia', S., Basili, V., Cantone, G. (2009). Scope error detection and handling concerning software estimation models. In Proceedings of the 3rd International Symposium on Empirical Software Engineering and Measurement (ESEM 2009) (pp.123-132). New York : IEEE CS [10.1109/ESEM.2009.5316020].
Scope error detection and handling concerning software estimation models
CANTONE, GIOVANNI
2009-09-01
Abstract
Over the last 25+ years, the software community has been searching for the best models for estimating variables of interest (e.g., cost, defects, and fault proneness). However, little research has been done to improve the reliability of the estimates. Over the last decades, scope error and error analysis have been substantially ignored by the community. This work attempts to fill this gap in the research and enhance a common understanding within the community. Results provided in this study can eventually be used to support human judgment-based techniques and be an addition to the portfolio. The novelty of this work is that, we provide a way of detecting and handling the scope error arising from estimation models. The answer whether or not scope error will occur is a pre-condition to safe use of an estimation model. We also provide a handy procedure for dealing with outliers as to whether or not to include them in the training set for building a new version of the estimation model. The majority of the work is empirically based, applying computational intelligence techniques to some COCOMO model variations with respect to a publicly available cost estimation data set in the PROMISE repository.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
