Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation

2020 ◽  
pp. 69-77
Author(s):  
Ajit Kumar Behera ◽  
Mrutyunjaya Panda
Keyword(s):  
Software Reliability ◽  
Ensemble Method ◽  
Reliability Prediction ◽  
Software Reliability Prediction ◽  
Data Point ◽  
Virtual Data

Efficient Software Reliability Prediction With Evolutionary Virtual Data Position Exploration

2021 ◽  
pp. 275-285
Author(s):  
Ajit Kumar Behera ◽  
Mrutyunjaya Panda
Keyword(s):  
Software Reliability ◽  
Prediction Models ◽  
Failure Time ◽  
Optimal Solution ◽  
Original Data ◽  
Reliability Prediction ◽  
Training Dataset ◽  
Software Reliability Prediction ◽  
Data Points ◽  
Virtual Data

Determining appropriate software reliability prediction technique is a challenging task for the software development process. So, it is essential for software engineers to develop good quality software product. Though several prediction models are in use for small size data, the estimation of the reliability of software system is crucial. Inadequate data may lead sub-optimal solution. This chapter proposes a technique of increasing training dataset by generating virtual data points original data. For improving the prediction of cumulative failure time in software, multilayer perceptron (MLP)-based virtual data positions (DEVDP) exploration techniques have been proposed. The parameters of the network are optimized by evolutionary algorithm differential evolution (DE). For validation of the model in presence of virtual data point (VDP), eight failure datasets from different sources has been used. The results obtained from the simulation studies indicate that proposed DEVDP exploration technique outperformed traditional models.

A hierarchical mixture model for software reliability prediction

2007 ◽  
Vol 185 (2) ◽  
pp. 1120-1130 ◽  
Author(s):  
Shaoming Li ◽  
Qian Yin ◽  
Ping Guo ◽  
Michael R. Lyu
Keyword(s):  
Mixture Model ◽  
Software Reliability ◽  
Reliability Prediction ◽  
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.

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