These applications include the characteristics such as NoSQL database, Big-Data Analytics, distributed file system and MapReduce architecture which may face issues like software aging due to which ongoing system's performance decreases and failure rate increases. Aging Related Bugs (ARB) are bugs that are caused due to memory leakage, null pointer exception, resource depletion etc. in the ongoing system whose impact can be dangerous, so it's better to predict them before releasing the software. Manual extraction of ARB reports are common but finding ARBs within thousand of bug reports is challenging. This is the first paper that presents the empirical study to automatically search aging related bug reports through SEARCH_KEYWORD algorithm and implement the ARB prediction in cross project for cloud oriented applications/softwares. To compare the efficacy of the prediction results, With-in Project Defect Prediction (WPDP) of ARBs is also performed. The work is divided in three phases: 1. ARB reports are extracted from the summary/description of bug in bug repository through automatic process. 2. Cross project bug prediction (CPDP) is performed to predict ARB due to limited availability of training data which is not implemented yet in cloud oriented softwares to the best of our knowledge. 3. Machine learning techniques are applied for ARB prediction to build fault prediction models. There is an imbalanced proportion between ARB-prone and ARB-free files, therefore Recall, FPR(False Positive Rate), Balance are used as major performance measures to predict ARBs. Kruskal Wallis Test and Friedman Test, are applied on the prediction results and it is proved that Naive Bayes performed significantly better than other classifiers. The results suggested that CPDP performed better than WPDP of ARBs using machine learning classifiers in cloud oriented datasets.Povzetek: Narejena je empirična analiza napovedovanja programskih napak v oblakih.
Good health refers to the mind and body's soundness and the state in which its tasks are carried out properly and effectively. Health is described by the World Health Organization as "a condition of complete physical, mental, and social well-being". There are a variety of elements that influence health in developing nations like India, such as poverty, food insecurity, food pricing and malnutrition, pollution and deterioration of the environment, occupational and reproductive health issues, cost prices of private health care systems, public health care delivery systems, and so on. The health of mothers, new-borns, and children is currently the prime agenda of multilateral organizations, international cooperation agencies, and governments around the world. Children are our future, and maintaining their health, development, and growth should be the top priority for all nations. Malnutrition, infectious diseases, being born underweight, maternal or neonatal or infant mortality, and other factors make new-borns and children more susceptible. This paper looks at the changes in the Mental Health Act 1987 to the new legislation of 2017 for the treatment and care of a mentally ill person and analyzes the various provisions of it from a psycho-socio-legal perspective.
Software aging is the process caused by Aging-Related Bugs (ARBs) which leads to the depletion of resources and degradation of performance in the long run. ARBs are difficult to find and replicate in future studies as they are less in number, thus prediction of ARB is necessary to save cost and time in the testing phase. ARBs are present in low proportion as compared to non-ARBs known as the class Imbalance problem resulting in insufficient training dataset for prediction models. In this study, Synthetic Minority Oversampling Technique (SMOTE) is applied along with homogeneous cross-project ARB prediction to reduce the effect of imbalance problem in software. SMOTE is oversampling of the minority instances synthetically to balance the dataset and improve the capability of defect prediction models. Homogeneous cross-project prediction is implemented where the datasets are different but the distribution of metric sets of both training and testing datasets is similar. The experiment is conducted on five cloud-oriented software such as Cassandra, Hive, Storm, Hadoop HDFS and Hadoop Mapreduce. The novelty of this study is the combination of SMOTE and homogeneous cross-project defect prediction for ARBs in cloud-oriented software. The comparative analysis is also conducted to understand the difference between SMOTE and non-SMOTE results with the help of machine learning classifiers. The result conveys that SMOTE is an efficient method to address class imbalance problem in ARB prediction.
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