Search results

The purpose of this paper is to present a novel method to estimate the optimal software testing time which minimizes the relevant expected software cost via a refined neural network approach with the grouped data, where the multi-stage look ahead prediction is carried out with a simple three-layer perceptron neural network with multiple outputs.

To analyze the software fault count data which follows a Poisson process with unknown mean value function, the authors transform the underlying Poisson count data to the Gaussian data by means of one of three data transformation methods, and predict the cost-optimal software testing time via a neural network.

In numerical examples with two actual software fault count data, the authors compare the neural network approach with the common non-homogeneous Poisson process-based software reliability growth models. It is shown that the proposed method could provide a more accurate and more flexible decision making than the common stochastic modeling approach.

It is shown that the neural network approach can be used to predict the optimal software testing time more accurately.

This study aims to find out the causes for an increase in the number illegal E-taxis and the extent of these vehicles in the municipalities of Bangladesh.

Based on extensive literature review and field investigation, a set of questionnaires was developed to explore the actual causes for an increase in the number of illegal E-taxis, where ten predicted hypotheses were tested.

The result proved that the illegal E-taxi is very active in the study area. Besides the socio-economic condition of the commuter, education level of taxi drivers and commuter satisfaction level (safety and comfort) and provision of continuous and door-to-door service system are the main causes for increasing number of E-taxis in the municipality of Bangladesh.

Moreover, this study provides an effective thinking on socio-economic condition of drivers and the legalization of illegal E-taxis in the study area.