An empirical study of software reliability prediction using machine learning techniques

2012 ◽  
Vol 3 (3) ◽  
pp. 194-208 ◽  
Author(s):  
Pradeep Kumar ◽  
Yogesh Singh
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Software Reliability ◽  
Machine Learning Techniques ◽  
Reliability Prediction ◽  
Learning Techniques ◽  
Software Reliability Prediction

Application of Machine Learning Techniques for Software Reliability Prediction (SRP)

2017 ◽  
pp. 113-142
Author(s):  
Pradeep Kumar
Keyword(s):  
Software Engineering ◽  
Software Reliability ◽  
Machine Learning Techniques ◽  
Reliability Prediction ◽  
Engineering Research ◽  
Reliability Models ◽  
Software Products ◽  
Software Reliability Prediction ◽  
Software Release ◽  
Software Engineering Research

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter on software reliability prediction using ANNs addresses three main issues: (1) analyze, manage, and improve the reliability of software products; (2) satisfy the customer needs for competitive price, on time delivery, and reliable software product; (3) determine the software release instance that is, when the software is good enough to release to the customer.

Application of Machine Learning Techniques to Predict Software Reliability

2012 ◽  
pp. 237-253
Author(s):  
Ramakanta Mohanty ◽  
V. Ravi ◽  
M. R. Patra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Software Reliability ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Learning Techniques ◽  
Trained Neural Network

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.

Application of Machine Learning Techniques to Predict Software Reliability

2010 ◽  
Vol 1 (3) ◽  
pp. 70-86 ◽  
Author(s):  
Ramakanta Mohanty ◽  
V. Ravi ◽  
M. R. Patra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Software Reliability ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Inference System ◽  
Learning Techniques

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.

Sign in / Sign up

Export Citation Format

Share Document