Proceedings of the 6th International Conference on Predictive Models in Software Engineering
On the value of outlier elimination on software effort estimation research
Empirical Software Engineering
Information and Software Technology
| Hi-index | 0.00 |
Accurate software effort estimation is one of the key factors to a successful project by making a better software project plan. To improve the estimation accuracy of software effort, many studies usually aimed at proposing novel effort estimation methods or combining several approaches of the existing effort estimation methods. However, those researches did not consider the distribution of historical software project data which is an important part impacting to the effort estimation accuracy. In this paper, to improve effort estimation accuracy by least squares regression, we propose a data partitioning method by the accuracy measures, MRE and MER which are usually used to measure the effort estimation accuracy. Furthermore, the empirical experimentations are performed by using two industry data sets (the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea).