Bayesian Inference Approach for Probabilistic Analogy Based Software Maintenance Effort Estimation

Abstract

Software maintenance effort estimation is essential for the success of software maintenance process. In the past decades, many methods have been proposed for maintenance effort estimation. However, most existing estimation methods only produce point predictions. Due to the inherent uncertainties and complexities in the maintenance process, the accurate point estimates are often obtained with great difficulties. Therefore some prior studies have been focusing on probabilistic predictions. Analogy Based Estimation (ABE) is one popular point estimation technique. This method is widely accepted due to its conceptual simplicity and empirical competitiveness. However, there is still a lack of probabilistic framework for ABE model. In this study, we first propose a probabilistic framework of ABE (PABE). The predictive PABE is obtained by integrating over its parameter k number of nearest neighbors via Bayesian inference. In addition, PABE is validated on four maintenance datasets with comparisons against other established effort estimation techniques. The promising results show that PABE could largely improve the point estimations of ABE and achieve quality probabilistic predictions.