Providing a Method for the Optimal Detection of Software Testing Paths Using Meta-Algorithms

Throughout the history of software engineering, the existence of software defects at the heart of a system and lack of proper treatment before operational use has always led to serious personal and financial      disasters. A test that can provide an appropriate coverage at the code-level of software can prevent many of these incidents. The basis path test is considered as the strongest coverage criterion in the white software box test. The prerequisite for a basis path testing is to have a set of test paths. The greater the number of test paths to be scanned, the greater the amount of software source code that will be covered and so more software holes will be discovered. As a result, a basic challenge before running a software path test is to produce the maximum test paths that can be scrolled. So far, some work has been done to maximize the number of scrollable test paths, including the GSO method, but the results indicate that the number of test paths can be greater than currently achieved. In this paper we have proposed a method to achieve this goal by a hybrid solution based on two evolutionary genetic and birds algorithms. The results of evaluations show that using the proposed solution has led to an increase in the number of scrollable test paths up to 91% comparing with the GSO method.