This paper is about integrating existing models in effort estimation literature using ensemble learning techniques.
Here you can see the preprint of the paper:
And here is a link to my presentation slides on this paper:
Predicting time and effort of software task completion has been an active area of research for a long time. Previous studies have proposed predictive models based on either text data or metadata of software tasks to estimate either completion time or completion effort of software tasks, but there is a lack of focus in the literature on integrating all sets of attributes together to achieve better performing models.
We first apply the previously proposed models on the datasets of two IBM commercial projects called RQM and RTC to find the best performing model in predicting task completion effort on each set of attributes. Then we propose an approach to create a hybrid model based on selected individual predictors to achieve more accurate and stable results in early prediction of task completion effort and to make sure the model is not bounded to some attributes and consequently is adoptable to a larger number of tasks. Categorizing task completion effort values into Low and High labels based on their measured median value, we show that our hybrid model provides 3-8% more accuracy in early prediction of task completion effort compared to the best individual predictors.