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Artificial Neural Network based Model for Reliability Assessment of Component based Software

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Title Artificial Neural Network based Model for Reliability Assessment of Component based Software
 
Creator Babu, Sumit
Singh, Raghuraj
 
Subject Complexity
Fuzzy
Machine learning
Reusability
Software quality
 
Description 617-626
Software quality assessment during early phases of software development process is an extremely important concern of
the researchers today because it identifies various aspects of quality degradation much prior to doing the actual damage to
the final product quality. This also serves as the basis for improvement of the process for developing software. Software
reliability is one of prime factor that affects software quality. In this paper, a model based on Artificial Neural Network
(ANN) for the assessment of reliability of Component Based Software Systems (CBSS) has been proposed. First, a
mathematical model based on formulation of software reliability in terms of the reliability factors using Analytical
Hierarchy Process (AHP) is developed. Further, this model is refined by using ANN to calculate appropriate weight values
reflecting true influence of reliability factors on software reliability. Model is validated by assessing the quality of 100
component based software using proposed models and an existing model. Results show a good correlation between the
mathematical model and the ANN model. The proposed AHP model and ANN model achieve higher average reliability
value of 0.5109 and 0.5088 respectively in comparison to the average reliability value of existing software reliability model
(0.2261). Precise assessment of software reliability through the proposed models during early stages of software
development helps developers to improve quality of software.
 
Date 2024-06-07T09:47:00Z
2024-06-07T09:47:00Z
2024-06
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64049
https://doi.org/10.56042/jsir.v83i6.5415
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(6) [June 2024]