Test Case Selection Technique implementation using Ant Colony Optimization to reduce the execution time of Regression Testing

Abstract

Software testing is an investigation conducted to provide stakeholders with information regarding the quality of the product or service under test. Software testing is especially important for guaranteeing software quality in associations. In fact, the quality of test suite plays an important role for the success of software testing. Regression testing, which is a type of software testing, is very expensive which is to be executing in time and resource constrained environment. Because of time and cost constraint, it’s not possible to complete extensive regression testing. There are various techniques which are used to solve the problem of time and cost constraints. Test Case Selection is one of the technique which helps in reducing the number of test cases by selecting only those test cases from the test suite which can detect all those faults which were detected by the whole test. There is an extraordinary desire in the field of Swarm Intelligence as it generally comes with a challenging task. Ant Colony Optimization (ACO) is one of challenging techniques for its provision in software testing.ACO is efficient algorithm to implement Test Case Selection technique by selecting test cases with higher probability. Our main objective of thesis is to reduce the execution time of Regression testing with the help of Ant Colony Optimization. In this research we are modifying the previous equation of probability calculate used in ACO up to now to get better results in case of execution time of Regression testing. We are implementing ACO using both previous and modified equation on some test cases and doing comparison of the results generated by them. Programming language used for implementing the Ant Colony Optimization algorithm is C# developed by Microsoft.