Software Effort Estimation (SEE) using Cross Company Data

There has been a long debate in the software engineering literature concerning how useful cross-company (CC) data are for software effort estimation (SEE) in comparison to within-company (WC) data. Studies indicate that models trained on CC data obtain either similar or worse performance than models trained solely on WC data. We aim at investigating how CC data could best be used for a company. A recently developed a new online ensemble learning approach, called Dycom, able to map CC data into a company's context so that other people's data can be used by me [1]. The key to the success of this approach is how well this mapping is learned and how well it captures the essence of the relationship between two companies' data. In its current implementation [1], such a mapping is assumed to be linear for simplicity reasons. This is obviously not a realistic assumption, because the relationship between two companies' data are often nonlinear. This project will use the Dycom framework but introduce nonlinear mappings int the algorithm. A tool for software effort estimation will be developed as part of the project. The project includes both creative thinking in terms of ideas, as well as software engineering of a tool. Reference: [1] L. Minku and X. Yao, "How to Make Best Use of Cross-company Data in Software Effort Estimation?" Proc. of the 36th International Conference on Software Engineering (ICSE'14), Hyderabad, India, 31/5-7/6/2014, pp.446-456, IEEE Press