Software companies exploit data about completed projects to estimate the development effort required for new projects. Software size is one of the most important information used to this end. However, different methods for sizing software exist and companies may require to migrate to a new method at a certain point. In this case, in order to exploit historical data they need to resize the past projects with the new method. Besides to be expensive, resizing is also often not possible due to the lack of adequate documentation. To support size measurement migration, we propose a transfer learning approach that allows to avoid resizing and is able to estimate the effort of new projects based on the combined use of data about past projects measured with the previous measurement method and projects measured with the new one. To assess our proposal, an empirical analysis is carried out using an industrial dataset of 25 projects. Function Point Analysis and COSMIC are the measurement methods taken into account in the study.
From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation / Corazza, Anna; DI MARTINO, Sergio; Ferrucci, Filomena; Gravino, Carmine; Sarro, Federica. - 9459:(2015), pp. 251-267. (Intervento presentato al convegno 16th International Conference on Product-Focused Software Process Improvement (PROFES 2015) tenutosi a Bolzano nel 2-4 December 2015) [10.1007/978-3-319-26844-6_19].
From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation
CORAZZA, ANNA;DI MARTINO, SERGIO;
2015
Abstract
Software companies exploit data about completed projects to estimate the development effort required for new projects. Software size is one of the most important information used to this end. However, different methods for sizing software exist and companies may require to migrate to a new method at a certain point. In this case, in order to exploit historical data they need to resize the past projects with the new method. Besides to be expensive, resizing is also often not possible due to the lack of adequate documentation. To support size measurement migration, we propose a transfer learning approach that allows to avoid resizing and is able to estimate the effort of new projects based on the combined use of data about past projects measured with the previous measurement method and projects measured with the new one. To assess our proposal, an empirical analysis is carried out using an industrial dataset of 25 projects. Function Point Analysis and COSMIC are the measurement methods taken into account in the study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
