Reliable Automated Software Testing Through Hybrid Optimization Algorithm

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.

Researches on Automatic Software Testing Techniques

This article briefly describes the background and the advantages of the automatic software testing technology research, summarizes the development, the features, and the research directions of the automatic software testing technology, and then example of analyses the testing process and factors of automatic software testing technology based on the C-ATFM automatic test model.

Application of Orthogonal Experimental Design for the Automatic Software Testing

According to inputting different combination of conditions so as to produce different impacts, software testing designs a large number of test cases. If the implementation of an overall test, due to the limit of the combination of conditions, it is difficult to carry out. In order to generate high quality test cases as early as possible to improve the efficiency of software testing, it is designed a generation tool of the automatic software testing case on orthogonal experimental design. For the test data, the use of that tool design test cases. The practice shows that a small number of test cases are generated, the error detection ability is strong, and it greatly improves the efficiency of software testing.