SOM-FTS: A Hybrid Model for Software Reliability Prediction and MCDM-Based Evaluation

Abstract

The objective of this study is to propose a hybrid model based on self-organized maps (SOM) and fuzzy time series (FTS) for predicting the reliability of software systems. The proposed SOM-FTS model is compared with eleven traditional machine learning-based models. The problem of selecting a suitable software reliability prediction model is represented as a multi-criteria decision-making (MCDM) problem. Twelve software reliability prediction models, including the proposed SOM-FTS model, are evaluated using three MCDM methods, four performance measures, and three software failure datasets. The results show that the proposed SOM-FTS model is the most suitable model among the twelve software reliability prediction models on the basis of MCDM ranking.

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

“…Song and Peng [19] evaluated various machinelearning algorithms for predicting financial risk. Kumar and Kaur [20] proposed the MCDM-based evaluation of different machine-learning algorithms for software reliability prediction. Ali et al [21] presented a precise MCDM method that empirically assesses and ranks classifiers, allowing end users to select the highest-ranked classifier for their application to train and create classification models.…”