Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/39042
Title: Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models
Authors: Minku, Leandro L.
Yao, X.
First Published: 28-Dec-2016
Publisher: Springer Verlag (Germany)
Citation: Automated Software Engineering (2016)
Abstract: Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by emphasizing the most useful past models. A thorough analysis of DCL’s behaviour is provided, strengthening its external validity.
DOI Link: 10.1007/s10515-016-0209-7
ISSN: 0928-8910
eISSN: 1573-7535
Links: http://link.springer.com/article/10.1007%2Fs10515-016-0209-7
http://hdl.handle.net/2381/39042
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © The Author(s) 2016 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Appears in Collections:Published Articles, Dept. of Computer Science

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