scholarly journals A New Method on Software Reliability Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Xiaonan ◽  
Yang Junfeng ◽  
Du Siliang ◽  
Huang Shudong
Keyword(s):  
Life Cycle ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Software Life Cycle ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Svm Model ◽  
Software Reliability Prediction ◽  
Simulation Results ◽  
Improved Model

As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. Firstly, the major disadvantages of the current software reliability models are discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established. Lastly, simulation results show that compared with classic models, the improved model has better prediction precision, better generalization ability, and lower dependence on the number of samples, which is more applicable for 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.

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.

scholarly journals Software Matrices Selection for a SDLC Based Software Reliability Prediction Model

2019 ◽  
Vol 8 (2S6) ◽  
pp. 959-963
Keyword(s):  
Software Reliability ◽  
Relevant Literature ◽  
Error Distribution ◽  
Reliability Estimation ◽  
Reliability Prediction ◽  
Development Phase ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Fault Density ◽  
Selection For

Software reliability is one of the essential factors of quality in software engineering like other quality attributes as functionality, usability, maintainability, performance, serviceability, documentation etc. From last few years, several software reliability models have been developed. There is lack of relevant literature which focuses on processes related to SDLC. A SDLC based structure for measurement of reliability has been proposed. Identified software reliability measures which are majorly take place in all levels of early software development phase of SDLC. Considering all measures for reliability estimation will be costly and time taking. So measures are identified which are taking place at each development phase and have high synthetic weight according to selecting criteria based on expert judgment and multi criteria decision making technique. Based on the grading, top ranked measures like completeness, error distribution, fault density etc are identified. Use of recommended metrics will make software reliability estimation more effective and reliable

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