How to Find Relevant Data for Effort Estimation?

Background: Building effort estimators requires the training data. How can we find that data? It is tempting to cross the boundaries of development type, location, language, application and hardware to use existing datasets of other organizations. However, prior results caution that using such cross data may not be useful. Aim: We test two conjectures: (1) instance selection can automatically prune irrelevant instances and (2) retrieval from the remaining examples is useful for effort estimation, regardless of their source. Method: We selected 8 cross-within divisions (21 pairs of within-cross subsets) out of 19 datasets and evaluated these divisions under different analogy-based estimation (ABE) methods. Results: Between the within & cross experiments, there were few statistically significant differences in (i) the performance of effort estimators, or (ii) the amount of instances retrieved for estimation. Conclusion: For the purposes of effort estimation, there is little practical difference between cross and within data. After applying instance selection, the remaining examples (be they from within or from cross source divisions) can be used for effort estimation.