Transfer learning in effort estimation
When projects lack sufficient local data to make predictions, they try to transfer information from other projects. How can we best support this process? In the field of software engineering, transfer learning has been shown to be effective for defect prediction. This paper checks whether it is possible to build transfer learners for software effort estimation. We use data on 154 projects from 2 sources to investigate transfer learning between different time intervals and 195 projects from 51 sources to provide evidence on the value of transfer learning for traditional cross-company learning problems. We find that the same transfer learning method can be useful for transfer effort estimation results for the cross-company learning problem and the cross-time learning problem. It is misguided to think that: (1) Old data of an organization is irrelevant to current context or (2) data of another organization cannot be used for local solutions. Transfer learning is a promising research direction that transfers relevant cross data between time intervals and domains.
transfer learning effort estimation data mining k-nn