Parameter Estimation of Software Reliability Using Soft Computing Techniques

2021 ◽  
pp. 329-343
Author(s):  
Sona Malhotra ◽  
Sanjeev Dhawan ◽  
Narender
Keyword(s):  
Parameter Estimation ◽  
Soft Computing ◽  
Software Reliability ◽  
Soft Computing Techniques

ON USING SOFT COMPUTING TECHNIQUES IN SOFTWARE RELIABILITY ENGINEERING

2006 ◽  
Vol 13 (01) ◽  
pp. 61-72 ◽  
Author(s):  
HENRIK MADSEN ◽  
POUL THYREGOD ◽  
BERNARD BURTSCHY ◽  
GRIGORE ALBEANU ◽  
FLORIN POPENTIU
Keyword(s):  
Soft Computing ◽  
Software Reliability ◽  
System Reliability ◽  
Time Series Prediction ◽  
Software Project ◽  
Reliability Engineering ◽  
Soft Computing Techniques ◽  
Software Fault ◽  
Computing Framework ◽  
Data Collections

Previous investigations have shown the importance of evaluating computer performances and predicting the system reliability. This paper considers soft computing techniques in order to be used for software fault diagnosis, reliability optimization and for time series prediction during the software reliability analysis. It is shown that the study of the data collections during a software project development can be done within a soft computing framework.

Soft Computing Techniques for Identification of an Asynchronous Machine Parameters

2014 ◽  
Vol 626 ◽  
pp. 155-163
Author(s):  
R. Sridevi ◽  
C. Kumar ◽  
N. Iswarya ◽  
R. Gopikaramanan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Soft Computing ◽  
Load Test ◽  
Artificial Immune ◽  
Swarm Optimization ◽  
Soft Computing Techniques ◽  
Equivalent Circuit Parameters

This manuscript discusses about the Parameter estimation of Induction motor by utilizing the soft computing methodologies that is by using evolutionary algorithms such as Genetic algorithm, Particle swarm optimization, Artificial immune algorithm to overcome the difficulties in the conventional method where we calculating the per phase equivalent circuit parameters from the No load test and Blocked rotor test which compromises in result in terms of accuracy of the result and also evaluated the accuracy of the different algorithm in estimating the parameters of the induction motor.

scholarly journals SRGM Dependent Learning based Testing Effort for Refinement

2021 ◽  
Vol 9 (11) ◽  
pp. 356-360
Author(s):  
S. Rumana Firdose
Keyword(s):  
Soft Computing ◽  
Software Reliability ◽  
The Other ◽  
Ann Model ◽  
Software Failure ◽  
Failure Data ◽  
Wide Range ◽  
Soft Computing Techniques ◽  
Computational Errors ◽  
Dependent Learning

Abstract: During the development of software code there is a pressing necessity to remove the faults or bugs and improve software reliability. To get the accurate result, in every phase of software development cycle assessments needs to be happen, so that in each phase early bugs detection takes place that leads to maintain accuracy at each level. The academic institutions and industries are enhancing the development techniques in software engineering and their by performing regular testing for finding faults in programmers of software during the development. New programs are composed by altered the original code by comprised more of a bias near statements that arise in pessimistic execution paths. Fault localization information technique is used in proposed method to indicate the position of fault. In experimental as well as regression based equations represent the soft computing techniques results is better compare to the other techniques. Evaluation of soft-computing techniques represented that accuracy of the ANN model is superior to the other models. Data bases for performing the training and testing stages were collected, these soft computing techniques had low computational errors than the empirical equations. Finally says that soft computing models are better compare to the regression models. Hence, finding faults and correcting a serious software problem would be better instead of recalling thousands of products, especially in automotive sector. SRGM success mainly reliable by gathering the accurate failure information. The functions of the software reliability growth model were predicted in terms of such information gathered only. SRGM techniques in the literature and it gives a reasonable capability of value for actual software failure data. Therefore, this model, in future, can be applied to operate a wide range of software and its applications. Keywords: SRGM, FDP, FCP

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques

2018 ◽  
Vol 42 (5) ◽  
Author(s):  
Chander Diwaker ◽  
Pradeep Tomar ◽  
Ramesh C. Poonia ◽  
Vijander Singh
Keyword(s):  
Soft Computing ◽  
Software Reliability ◽  
Soft Computing Techniques
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