ENHANCING SOFTWARE RELIABILITY OF A COMPLEX SOFTWARE SYSTEM ARCHITECTURE USING ARTIFICIAL NEURAL-NETWORKS ENSEMBLE
| Autor: | Sunil Kumar Khatri, V. S. Sarma Yadavalli, Mashaallah Basirzadeh, P. K. Kapur |
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| Rok vydání: | 2011 |
| Předmět: |
General Computer Science
Computer science business.industry Energy Engineering and Power Technology Aerospace Engineering Machine learning computer.software_genre Industrial and Manufacturing Engineering Software quality Software metric Reliability engineering Software Nuclear Energy and Engineering Software sizing Goal-Driven Software Development Process Software construction Software verification and validation Artificial intelligence Software reliability testing Electrical and Electronic Engineering Safety Risk Reliability and Quality business computer |
| Zdroj: | International Journal of Reliability, Quality and Safety Engineering. 18:271-284 |
| ISSN: | 1793-6446 0218-5393 |
| DOI: | 10.1142/s0218539311004135 |
| Popis: | Modeling of software reliability has gained lot of importance in recent years. Use of software-critical applications has led to tremendous increase in amount of work being carried out in software reliability growth modeling. Number of analytic software reliability growth models (SRGM) exists in literature. They are based on some assumptions; however, none of them works well across different environments. The current software reliability literature is inconclusive as to which models and techniques are best, and some researchers believe that each organization needs to try several approaches to determine what works best for them. Data-driven artificial neural-network (ANN) based models, on other side, provide better software reliability estimation. In this paper we present a new dimension to build an ensemble of different ANN to improve the accuracy of estimation for complex software architectures. Model has been validated on two data sets cited from the literature. Results show fair improvement in forecasting software reliability over individual neural-network based models. |
| Databáze: | OpenAIRE |
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