A Study of Applying Deep LearningBased Weighted Combinations to Improve Defect Prediction Accuracy and Effectiveness Software errors or bugs are the primary cause of poor software quality. Thus defect prediction is a prominent approach to enhance software quality. It is a common technique for identifying defectprone programs, which help the practitioners allocate needed quality assurance efforts (e.g., testing and debugging). An accurate prediction may bring significant benefits. However, there is still space for improvements by applying different levels of instances or using some stateoftheart techniques to construct the prediction models. In this paper, we propose a weighted combination method of activation functions to improve the effectiveness of defect prediction, comprising of weighted arithmetic, geometric, harmonic, contraharmonic, and cubic combinations. When there are several kinds of classifiers, the method of weighted combinations can be applied to combine the strengths and create a new model or classifier. That is, weighted combination method(s) would be able to combine the advantage of different activation functions to train the neural network in deep learning. Six opensource projects are used to to evaluate the performance of weighted combination methods of activation functions: single, double and tripleweighted approaches. Experimental results show that doubleweighted combinations of activation function outperform single and tripleweighted combinations. It is also worth noting that single activation function outperform tripleweighted combination.

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.