Software reliability models are usually used to model the failure of software systems and prediction of its reliability potential. These models are however plagued with less accuracy, efficiency, and resource-effectiveness. Some soft computing methods have not yet been implemented to investigate their effectiveness and robustness for software fault prediction. Pi Sigma Neural Network (PSNN) software reliability prediction model was developed in this study for a better understanding of the modelling of software systems defects and reliability validated on 5 NASA promise datasets after carrying out data analysis using Seaborn on Python, working with raw data, pre-processed data with min-max normalization, Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalance problem between defective and non-defective modules, and then correlational analysis with varying thresholds (0.8, 0.85, 0.9 and 0.95) to reduce noise and get key features. The results obtained using the PSNN model showed for all the datasets good average performance for recall being highest at 79.8% based on no threshold, precision at 76.2% on 0.9 threshold, f1-score with 75.6% on 0.95 threshold and accuracy at 74.8% with the same 0.95 threshold. A model based on recall is good at fault finding. Modifying the structure and architecture of the PSNN, like using a voting ensemble algorithm of varied combinations of PSNNs and using a firefly algorithm to optimize in the future, will improve the Neural Network technique

The coronavirus disease (SARS-CoV-2)) pandemic has caused unprecedented economic crises, and changes in our lifestyle to different things that we have not experienced before in this century, which cause by movement restriction order by the authority to halt the spread of the disease around the globe. Researchers around the globe applied computational intelligence methods in numerous fields which exhibits a successful story. The computational intelligence methods play an important role in dealing with coronavirus pandemics. This research will focus on the use of computational intelligence methods in understanding the infection, accelerating drugs and treatments research, detecting, diagnosis, and predicting the virus, surveillance, and contact tracing to prevent or slow the virus from the spread, monitoring the recovery of the infected individuals. This study points out promising CI techniques utilized as an adjunct along with the current methods used in containments of COVID-19. It is imagined that this study will give CI researchers and the wider community an outline of the current status of CI applications and motivate CI researchers in harnessing CI technique possibilities in the battle against COVID-19.