Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3600
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dc.contributor.authorKumar, L-
dc.contributor.authorRath, S K-
dc.date.accessioned2021-12-20T06:55:52Z-
dc.date.available2021-12-20T06:55:52Z-
dc.date.issued2014-
dc.identifier.citationINFOCOMP Journal of Computer Science, Vol 13, Iss 2 P 10-21en_US
dc.identifier.issn1807-4545-
dc.identifier.urihttp://hdl.handle.net/2080/3600-
dc.description.abstractEstimation of different parameters for object-oriented systems development such as effort, quality, and risk is of major concern in software development life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. Also it is observed that numerous software metrics are being used as input for estimation. In this study, object-oriented metrics have been considered to provide requisite input data to design the models for prediction of maintainability using three artificial intelligence (AI) techniques such as neural network, Neuro-Genetic (hybrid approach of neural network and genetic algorithm) and Neuro-PSO (hybrid approach of neural network and Particle Swarm Optimization). These three AI techniques are applied to predict maintainability on two case studies such as User Interface System (UIMS) and Quality Evaluation System (QUES). The performance of all three AI techniques were evaluated based on the various parameters available in literature such as mean absolute error (MAE) and mean Absolute Relative Error (MARE). Experimental results show that the hybrid technique utilizing Neuro-PSO technique achieved better result for prediction of maintainability when compared with the other two.en_US
dc.language.isoenen_US
dc.publisherINFOCOMP Journal of Computer Scienceen_US
dc.subjectArtificial neural networken_US
dc.subjectsoftware metricsen_US
dc.subjectGenetic algorithmen_US
dc.subjectmaintainabilityen_US
dc.subjectNeuronsen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectQUESen_US
dc.subjectUIMSen_US
dc.titleHybrid neural network approach for predicting maintainability of object-oriented softwareen_US
dc.typeArticleen_US
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