Open AccessReliable Automated Software Testing Through Hybrid Optimization Algorithm
Author(s) -
Subarna Shakya,
S. Smys
Publication year - 2020
Publication title -
journal of ubiquitous computing and communication technologies
Language(s) - English
Resource type - Journals
ISSN - 2582-337X
DOI - 10.36548/jucct.2020.3.002
Subject(s) - computer science , reliability engineering , non regression testing , software reliability testing , particle swarm optimization , reliability (semiconductor) , software , software performance testing , software quality , process (computing) , test strategy , algorithm , software development , software construction , engineering , power (physics) , physics , operating system , quantum mechanics , programming language
Software testing is an important process in product development of software companies to ensure the product quality. The developed application must satisfy the customer needs and meets the industrial standards without any compromise. Thus, verification of products through manual test engineer and validate the product once it meets all the necessary requirements. The issues in manual testing is its high computation and analysis time, accuracy and reliability. In order to reduce the issues in manual testing automatic testing is introduced. Though it is also an application which requires parameters to test the given product. Efficient tuning of the product could be achieved through automatic testing. This proposed research work provides an optimized automatic software testing model through differential evolution and ant colony optimization as a hybrid model to achieve improved accuracy and reliability in software testing. Conventional models like artificial neural network and particle swarm optimization are compared with proposed model to validate the reliability of proposed model.