Modeling Attributes Influencing Intelligent Information Software System using Exploratory Factor Analysis
Manu Banga1, Abhay Bansal2, Archana Singh3
1Manu Banga, ASET, Amity University, Noida, India.
2Abhay Bansal, ASET, Amity University, Noida, India.
3Archana Singh, ASET, Amity University, Noida, India.
Manuscript received on 09 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript published on 30 June 2019 | PP: 2494-2498 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6881068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s era Software development is hybrid with Software Reliability Model, using model parameters for validating software faulty dataset as it has many pitfalls choosing reliability model being dataset biasing, parameters on which choosing parameter estimation relevant to model based on statistical test, readiness index, functionality preserving, effort consumed on faulty datasets prediction. Model Estimation Modeling based on Least Square Error (LSE) for which factor analyzed using Exploratory Factor Analysis and Confirmatory Factor Analysis and model is validated using p-test with confidence interval of 95% on cloud service. Based on our eleven attribute of our dataset, Continuous Integration is most relevant among other factors. Defect Prediction is one the most crucial and critical task in successful operation of software working as can leads to faults which further causes failures and these failures are very ominous, thus reducing and preventing software defects is major challenging work
Keywords: Software Reliability Models, Cloud Service, EFA, CFA, Intelligent Information System.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques