Open source software reliability model with nonlinear fault detection and fault introduction

2021 ◽  
Author(s):  
Jinyong Wang
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Reliability Model ◽  
Software Reliability Model

scholarly journals NHPP Software Reliability Model with Inflection Factor of the Fault Detection Rate Considering the Uncertainty of Software Operating Environments and Predictive Analysis

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 521 ◽  
Author(s):  
Song ◽  
Chang ◽  
Pham
Keyword(s):  
Fault Detection ◽  
Software Reliability ◽  
Detection Rate ◽  
The Other ◽  
Reliability Model ◽  
New Model ◽  
Software Failures ◽  
Software Failure ◽  
Software Reliability Model ◽  
Operating Environments

The non-homogeneous Poisson process (NHPP) software has a crucial role in computer systems. Furthermore, the software is used in various environments. It was developed and tested in a controlled environment, while real-world operating environments may be different. Accordingly, the uncertainty of the operating environment must be considered. Moreover, predicting software failures is commonly an important part of study, not only for software developers, but also for companies and research institutes. Software reliability model can measure and predict the number of software failures, software failure intervals, software reliability, and failure rates. In this paper, we propose a new model with an inflection factor of the fault detection rate function, considering the uncertainty of operating environments and analyzing how the predicted value of the proposed new model is different than the other models. We compare the proposed model with several existing NHPP software reliability models using real software failure datasets based on ten criteria. The results show that the proposed new model has significantly better goodness-of-fit and predictability than the other models.

Reliability Modeling and Assessment for Open Source Cloud Software

2015 ◽  
pp. 1069-1090
Author(s):  
Yoshinobu Tamura ◽  
Shigeru Yamada
Keyword(s):  
Cloud Computing ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Network Traffic ◽  
Reliability Model ◽  
Neural Network Approach ◽  
Reliability Modeling ◽  
Software Products ◽  
Cost Efficiencies

Software development based on the Open Source Software (OSS) model is being increasingly accepted to stand up servers and applications. In particular, Cloud OSS is now attracting attention as the next generation of software products due to cost efficiencies and quick delivery. This chapter focuses on the software reliability modeling and assessment for Cloud computing infrastructure software, especially open source software, such as OpenStack and Eucalyptus. In this chapter, the authors introduce a new approach to the Jump diffusion process based on stochastic differential equations in order to consider the interesting aspect of the numbers of components and users in the reliability model. In addition, the authors consider the network traffic of the Cloud in the reliability modeling and integrate the reliability model with a threshold-based neural network approach that estimates network traffic. Actual software fault-count data are analyzed in order to show numerical examples of software reliability assessment. This chapter also illustrates how the proposed method of reliability analysis can assist in quality improvement in Cloud computing software.

Open Source Software Reliability Model with the Decreasing Trend of Fault Detection Rate

2018 ◽  
Vol 62 (9) ◽  
pp. 1301-1312
Author(s):  
Jinyong Wang ◽  
Xiaoping Mi
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Real World ◽  
Detection Rate ◽  
Failure Process ◽  
Model Performance ◽  
Predictive Performance ◽  
Proposed Model

Abstract Software reliability assessment methods have been changed from closed to open source software (OSS). Although numerous new approaches for improving OSS reliability are formulated, they are not used in practice due to their inaccuracy. A new proposed model considering the decreasing trend of fault detection rate is developed in this study to effectively improve OSS reliability. We analyse the changes of the instantaneous fault detection rate over time by using real-world software fault count data from two actual OSS projects, namely, Apache and GNOME, to validate the proposed model performance. Results show that the proposed model with the decreasing trend of fault detection rate has better fitting and predictive performance than the traditional closed source software and other OSS reliability models. The proposed model for OSS can further accurately fit and predict the failure process and thus can assist in improving the quality of OSS systems in real-world OSS projects.

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