An SRGM for Multi-Release Open Source Software System

A number of software reliability growth models have been reported in the literature for open source software (OSS) systems but the effect of up-gradations on the reliability growth of multi-releases of such software systems has been discussed by a few. In this paper, the discrete modeling framework has been proposed to study the reliability growth process of OSS systems with multiple releases. The proposed model is based upon the assumption that during up-gradation some new faults are introduced in the code in addition to the left over fault content of the previous version. To validate our model, we have chosen two successful open source projects-Mozilla and Apache for its multi release failure datasets. Graphs representing goodness of fit of the proposed model have been drawn. The parameter estimates and measures of goodness of fit criteria suggest that the proposed software reliability growth model for multi-release OSS fits the actual datasets very well. An optimal release policy has been formulated by taking into account the cost of fault removal during testing and operational phases and reliability targets pre-specified by the decision makers. In addition, numerical example along with the sensitivity analysis has been provided to illustrate optimal release policy.

Optimal Release Policy for Multi-Release Software System

In software industry, multi release software development process is a latest phenomenon that brings the benefits of newer technologies, while retaining the quality. In this paper, it is assumed that the development of next version or release starts immediately after the launch of the previous version and the field test of each version continues after its release so that undetected faults of just previous version along with the added faults of latest version are detected during the testing of new software code. Today's dynamic customers need timely up gradation. Therefore, to sustain user growth and satisfaction it is imperative for the developers to know the appropriate time to launch upgraded software into the market. In this paper, an optimal release policy for multi release software system has been proposed by taking into consideration the testing as well as the operational phase. A numerical example has been presented to illustrate the optimal release policy. Parameter estimation has been done using the real-life fault data set. Goodness-of-fit curves have been drawn.

Augmentation of an artificial neural network and modified stochastic dynamic programing model for optimal release policy

In this paper, a comprehensive modified stochastic dynamic programing with artificial neural network (MSDP-ANN) model is developed and applied to derive optimal operational strategies for a reservoir. Most water resource problems involve uncertainty. To show that the MSDP-ANN model addresses uncertainty in the input variable, the result of the MSDP-ANN model is compared with the performance of a detailed conventional stochastic dynamic programing with regression analysis (CSDP-RA) model. The computational time of the CSDP-ANN model is modified with concave objective functions by deriving a monotonic relationship between the reservoir storage and optimal release decision, and an algorithm is proposed to improve the computational efficiency of reservoir operation. Various indices (i.e. reliability, vulnerability, and resiliency) were calculated to assess the model performance. After comparing the performance of the CSDP-RA model with that of the MSDP-ANN model, it was observed that the MSDP-ANN model produces a more reliable and resilient model and a smaller supply deficit. Thus, it can be concluded that the MSDP-ANN model performs better than the CSDP-RA model in deriving the optimal operating policy for the reservoir.