Application of Machine Learning Techniques to Predict Software Reliability

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second. DOI: 10.4018/jaec.2010070104 International Journal of Applied Evolutionary Computation, 1(3), 70-86, July-September 2010 71 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. important factor which is related to defects and faults. It differs from hardware reliability in that it reflects the design perfection, rather than manufacturing perfection. The principal factors that affect software reliability are (i) fault introduction (ii) fault removal and (iii) the environment. Fault introduction depends primarily on the characteristics of the product and the development process. The characteristics of development process include software engineering technologies and tools used the level of experience of the personnel, volatility of requirements, and other factors. Failure discovery, in turn, depends on the extent to which the software has been executed and the operational profile. Because some of the foregoing factors are probabilistic in nature and operate over time, software reliability models have generally been formulated in terms of random processes in execution time. In the past few years much research work has been carried out in software reliability and forecasting but no single model could capture software characteristics. In this paper, we investigate the performance of some of the well known machine learning techniques in predicting software reliability. The rest of the paper is organized as follows. In Literature review section, a brief review of the works carried out in area of software reliability prediction is presented. In the next section, the various stand-alone machine learning techniques applied in this paper are briefly described. In the next section, the experimental design followed in this paper is presented. It is followed by a section that discusses the results obtained. Finally, the last section concludes the paper.