A software reliability model for open source big data system considering fault introduction and fault removal efficiency

Abstract

Abstract Reliability is critical to the stable operation of open source big data system software. So far, the reliability modeling and evaluation of open source big data system software is still in its early stages, and traditional software reliability models are not suitable for the development and testing environment of open source big data system software. Both closed source and open source software development and testing environments cannot be consistent with big data system software development and testing environments, and open source big data system software development and testing environments are even more complex. Such as, dynamic and heterogeneous environments, evolving architectures, and interdependencies and ecosystem complexity, etc. Therefore, traditional software reliability models fail to meet the reliability evaluation requirements for open source big data system software. In this paper, we focus on the characteristics of open source big data system software development and testing, such as the complexity of fault detection, the possibility of introducing new faults and fault removal efficiency factors during fault debugging, to establish a new software reliability model. By comparing with established software reliability models, the accuracy of the proposed model in predicting faults is verified, and the proposed model can effectively evaluate the reliability of open source big data system software. The proposed model can be used for fault prediction and reliability evaluation of open source big data system software in practical development.

Keywords

• Software reliability model
• Fault removal efficiency
• Fault introduction
• Open source big data system