Revisiting the conclusion instability issue in software effort estimation

Abstract

Conclusion instability is the absence of observing the same effect under varying experimental conditions. Deep Neural Network (DNN) and ElasticNet software effort estimation (SEE) models were applied to two SEE datasets with the view of resolving the conclusion instability issue and assessing the suitability of ElasticNet as a viable SEE benchmark model. Results were mixed as both model types attain conclusion stability for the Kitchenham dataset whilst conclusion instability existed in the Desharnais dataset. ElasticNet was outperformed by DNN and as such it is not recommended to be used as a SEE benchmark model.