Propheticus: Machine Learning Framework for the Development of Predictive Models for Reliable and Secure Software The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently used in the dependability domain, both for supporting systems design and verification activities. However, using ML is complex and highly dependent on the problem in hand, increasing the probability of mistakes that compromise the results. In this paper, we introduce Propheticus, a ML framework that can be used to create predictive models for reliable and secure software systems. Propheticus attempts to abstract the complexity of ML whilst being easy to use and accommodating the needs of the users. To demonstrate its use, we present two case studies (vulnerability prediction and online failure prediction) that show how it can considerably ease and expedite a thorough ML workflow.

On Fault Localization Using Machine Learning Techniques Early prediction of faulty modules provides a way to support software quality assurance activities through improved scheduling and effectiveness of process control. Different fault prediction models have been proposed by researchers for early prediction of faults. However, despite predicting faulty classes, these techniques donu0027t provide information about identifying the location of occurrence of faults. In this paper, we aim to propose a methodology not only to predict faults but also for fault localization as well. We first make use of various datasets to build a software fault prediction model based. We make use of random forest machine learning technique to train our model. We also extract CKmetrics from different modules and we then make use of these metrics for fault localization. In order to do validation, we make use of case studies where we do fault prediction, fault localization and then run test cases to see if our prediction was correct.