An Empirical Study of Aging Related Bug Prediction Using Cross Project in Cloud Oriented Software

Abstract

Cloud oriented applications enable users to increase the scalability of computing infrastructure by using shared computer resources. 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.