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Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models

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journal contribution
posted on 04.01.2017, 10:11 by Leandro L. Minku, X. Yao
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.

Funding

This work was supported by an EPSRC Grant (No. EP/J017515/1).

History

Citation

Automated Software Engineering (2016)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Version

VoR (Version of Record)

Published in

Automated Software Engineering (2016)

Publisher

Springer Verlag (Germany)

issn

0928-8910

eissn

1573-7535

Acceptance date

07/12/2016

Copyright date

2016

Available date

04/01/2017

Publisher version

http://link.springer.com/article/10.1007/s10515-016-0209-7

Language

en