A dynamic particle filter-support vector regression method for reliability prediction

In this paper, ensemble models are developed to accurately forecast software reliability. Various statistical (multiple linear regression and multivariate adaptive regression splines) and intelligent techniques (backpropagation trained neural network, dynamic evolving neuro-fuzzy inference system and TreeNet) constitute the ensembles presented. Three linear ensembles and one non-linear ensemble are designed and tested. Based on the experiments performed on the software reliability data obtained from literature, it is observed that the non-linear ensemble outperformed all the other ensembles and also the constituent statistical and intelligent techniques.

Software reliability prediction by soft computing techniques

The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron’s (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

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Software reliability prediction is very important for minimising cost and improving the effectiveness of the software development process. As an important method, relative data during software lifecycle is used to analyse and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve non-linear predicting problems in many fields such as software reliability prediction and has obtained well performance in many situations, and it is still difficult to select its parameters. Previously, intelligence optimisation algorithms, such as genetic algorithm (GA), are mostly used for finding better parameters of SVR, however existing methods of selecting parameters require usually has some disadvantages. In this study, to overcome weaknesses of GA, such as the local minima and the premature convergence problems, GA and simulated annealing (SA) are integrated into a new optimise algorithm, called GA-SA, it is then applied to SVR for predicting software reliability. The authors compare proposed GA-SA-SVR model with other software reliability models through real software failure data. The experimental results show that the proposed GA-SA-SVR model can obtain better predictions results than the other models and has a fairly accurate prediction capability.

Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm

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Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm