Heterogeneous Cross-Company Effort Estimation through Transfer Learning

Abstract

Software effort estimation is vital but challenging activity during software development. In many small or medium-sized companies, such challenges are stemmed from historical data shortage. The problem can be solved by leveraging cross-company data for effort estimation. While in practice, cross-company effort estimation may not be easy to take because the cross-company data for effort estimation can be heterogenous. In this paper, we propose a novel approach named Mixture of Canonical Correlation Analysis and Restricted Boltzmann Machines (MCR) to address data heterogeneity issue in cross-company effort estimation. The essential ideas in MCR are (1) to present a unified metric representing heterogenous effort estimation data; and (2) to combine Canonical Correlation Analysis and Restricted Boltzmann Machines method to estimate effort in heterogenous cross-company effort estimation. The MCR approach is evaluated on 5 public datasets in PROMISE repository. The evaluation results show that: (1) for estimations with partially different metrics, the MCR approach outperforms within-company effort estimator KNN with a decrease in MMRE by 0.60, an increase in PRED(25) by 0.16, and a decrease in MdMRE by 0.19; (2) for estimations with totally different metrics, the MCR approach outperforms within-company effort estimator KNN with a decrease in MMRE by 0.49, an increase in PRED(25) by 0.08, and a decrease in MdMRE by 0.10.