Abstract

The paper deals with a review of the current software of estimation of the green software reliability. The paper demonstrates that any software is capable of becoming a “green” one if it is at the high reliability level, which has to be continuously measured and controlled. In order to implement these actions, the paper offers the results of reviewing the most frequently used software performing the reliability estimation. On the basis of the analysis of this software characteristics a taxonomic system of classification features describing its (software) functionality and the ease of presentation of the estimation results with the help of this software is offered. By using the offered taxonomy a system of classification of software reliability estimation tools was created. The conclusions of the current trends of research in the area of reliability estimation and green software development were made.

Highlights

  • At present any software is considered to be a green one if the electric energy is saved in using this type of software

  • The paper offers an hierarchic group of classification features – taxons, which represent the Software reliability estimation tools and their main characteristics in the most complete and correct way

  • The usage of the developed taxonometric features allows to create a classification of the current Software tools

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Summary

Introduction

At present any software is considered to be a green one if the electric energy is saved in using this type of software. The manual calculation of these coefficients takes a lot of time, and this fact became an impulsive cause for creation of a large number (more than twenty) of program tools for software reliability estimation. Each of these software tools as a rule permits for estimating the reliability according to not one but a significant number of SPMs. The availability of the variety of program tools makes some difficulties for a developer in choosing the best and most suitable SRGM of the amount of implemented in it (program tool).

The review of reliability estimation program tools
Classification features formation
Conclusion
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