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.

Simple Plots Improve Software Reliability Prediction Models

2003 ◽  
Vol 15 (3) ◽  
pp. 411-417 ◽  
Author(s):  
John S. Lawson ◽  
Craig W. Wesselman ◽  
Del T. Scott
Keyword(s):  
Software Reliability ◽  
Prediction Models ◽  
Reliability Prediction ◽  
Software Reliability Prediction

SOFTWARE RELIABILITY PREDICTION USING RECURRENT NEURAL NETWORK WITH BAYESIAN REGULARIZATION

2004 ◽  
Vol 14 (03) ◽  
pp. 165-174 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Failure Time ◽  
Reliability Prediction ◽  
Neural Network Modeling ◽  
Bayesian Regularization ◽  
Neural Network Models ◽  
Software Reliability Prediction

A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.

scholarly journals ENHANCED ALGORITHM OF ARTIFICIAL BEE COLONY (ABC) TO OPTIMIZE MODELS OF SYSTEM RELIABILITY

2021 ◽  
Vol 9 (01) ◽  
pp. 835-866
Author(s):  
Samuel Acquah ◽  
◽  
Li Zhen ◽  
Anastasia Krampah-Nkoom ◽  
◽  
...  
Keyword(s):  
Software Quality ◽  
Software Reliability ◽  
Software Metrics ◽  
Artificial Bee Colony ◽  
Prediction Models ◽  
Back Propagation ◽  
Reliability Prediction ◽  
Bee Colony ◽  
Software Reliability Models ◽  
Software Reliability Prediction

In recent times, computer software applications are increasingly becoming an essential basis in several multipurpose domains including medicine, engineering, transportation etc. Consequently, with such wide implementation of software, the imperative need of ensuring certain software quality physiognomies such as efficiency, reliability and stability has ascended. To measure such software quality features, we have to wait until the software is executed, tested and put to use for a certain period of time. Numerous software metrics are presented in this study to circumvent this long and expensive process, and they proved to be awesome method of estimating software reliability models. For this purpose, software reliability prediction models are built. These are used to establish a relationship between internal sub-characteristics such asinheritance, coupling, size, etc. and external software quality attributes such as maintainability, stability, etc. Usingsuchrelationships, one canbuildamodelinordertoestimatethereliabilityofnewsoftware system.Suchmodelsaremainlyconstructedbyeitherstatisticaltechniquessuchasregression,or machine learningtechniquessuchasC4.5andneuralnetworks.The prototype presented isinvigoratedemployingprocedures of machine learninginparticularrule-basedmodels.Thesehaveawhite-boxnaturewhich accordsthecataloguingandmakingthemgood-looktoexpertsinthedomain. In this paper, wesuggest a powerfulinnovative heuristic based on Artificial Bee Colony (ABC) to enhance rule-based software reliability prediction models. The presented approach is authenticated on data describing reliability of classes in an Object-Oriented system. We compare our models to others constructed using other well-established techniques such as C4.5, Genetic Algorithms (GA), Simulated Annealing (SA), Tabu Search (TS), multi-layer perceptron with back-propagation,multi-lay perceptron hybridized with ABC and the majority classifier. Results show that, in most cases, the propose technique out- performs the others in different aspects.

DYNAMIC SOFTWARE RELIABILITY PREDICTION: AN APPROACH BASED ON SUPPORT VECTOR MACHINES

2005 ◽  
Vol 12 (04) ◽  
pp. 309-321 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Failure Time ◽  
Reliability Prediction ◽  
Support Vector ◽  
Failure Time Data ◽  
Time Data ◽  
Software Failure ◽  
Learning Scheme ◽  
Software Reliability Prediction

A support vector machine (SVM) modeling approach for software reliability prediction is proposed. Based on the structural risk minimization principle, the learning scheme of SVM is focused on minimizing an upper bound of the generalization error that eventually results in better generalization performance. The SVM learning scheme is applied to the failure time data, forcing the network to learn and recognize the inherent internal temporal property of software failure sequence. Further, the SVM learning process is iteratively and dynamically updated after every occurrence of new failure time data in order to capture the most current feature hidden inside the software failure behavior. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets and compared with feed-forward neural network and recurrent neural network modeling approaches. Experimental results show that our proposed approach adapts well across different software projects, and has a better next-step prediction performance.

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