Ensemble Balanced Nested Dichotomy Fuzzy Models for Software Requirement Risk Prediction

Abstract

Modern software systems are becoming more intricate, making identification of risks in the software requirement phase— a fundamental aspect of the software development life cycle (SDLC)—complex. Inadequate risk assessment may result in the malfunction of a software system, either in the development or production phase. Therefore, risk prediction plays a crucial role in software requirements, serving as the first step in any software project. Hence, developing adaptive predictive models that can offer consistent and explainable insights for handling risk prediction is imperative. This study proposes novel ensemble class balanced nested dichotomy (EBND) fuzzy induction models for risk prediction in software requirement. Specifically, the proposed EBND models employ a hierarchical structure consisting of binary trees featuring distinct nested dichotomies that are generated randomly for each tree. Thereafter, we use an ensemble principle to refine rules generated from the resulting binary tree. The predictive efficacy of the suggested EBND models is further extended by introducing a data sampling method into their prediction process. The inclusion of the data sampling method acts to mitigate the underlying disparity in the class labels that may affect its prediction processes. The efficacy of the EBND models is then evaluated and compared to current solutions using the open-source software risk dataset. The observed findings revealed that the EBND models demonstrated superior predictive capabilities when compared to the conventional models and state-of-the-art methodologies. Specifically, the EBND models achieved an average accuracy threshold value of 98%, as well as high values for the f-measure metric.

Keywords

• Software requirement
• software risk prediction
• machine learning
• ensemble learning