Open AccessA New Method to Predict the Software Fault Using Improved Genetic Algorithm
Author(s) -
Fahimeh Sadat Fazel
Publication year - 2016
Publication title -
bulletin de la société royale des sciences de liège
Language(s) - English
Resource type - Journals
ISSN - 1783-5720
DOI - 10.25518/0037-9565.5275
Subject(s) - troubleshooting , reliability engineering , computer science , software , software sizing , software quality , software reliability testing , reliability (semiconductor) , process (computing) , avionics software , software development , software system , identification (biology) , software construction , software metric , genetic algorithm , machine learning , engineering , operating system , power (physics) , physics , botany , quantum mechanics , biology
Maintaining and repairing the engineering machinery will be optimal when identification of defect or troubleshooting process, error analysis and fix it and in other word repair process, perform in highest precise, low time and cost. However speed and precise in troubleshooting process, defect analysis and repairing cause to decrease costs. So, the repair and troubleshooting role in a repair and maintenance system is so significant. While today’s, the software has a main role to perform systems tasks, so they have a high importance in systems reliabilities. Therefore, to increase reliability, it is necessary to design systems against error if tolerable. With regard to increasingly development and applying software in different domains, software reliability has an important role during software life time. One of the most important solutions to increase software reliability is predicting software errors that cause to decrease in software maintenance cost in the future. There are many ways to predict software errors. Due to intelligence algorithms such as genetics algorithms, have a high ability to predict, so we can use them to predict software future condition or predict software errors. In this paper, we present a method to predict software error via genetics algorithm. The aim of this method is to predict software error with a higher precise and speed, and also to present structure that be implementation and expansion easily. The attained results show a desired performance of this method from time period for predicting error and output rate or recognition. The results show the recognition rates of suggested method more that 95 percent in best condition.