A Method of Fault Identification Considering High Fix Priority in Open Source Project Open source software is adopted as embedded systems, server usage and so on because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method, in which faults are found and fixed by developers around the world, and the result will be reflected in the next release. However, several massive open source projects have a problem that faults fixing takes a lot of time because faults corrector cannot handle many faults reports briefly. In this paper, we make an index to detect faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved compared with using only specific version data.

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.