Robot automation testing of software using genetic algorithm

Abstract

The demand for excellent software has significantly increased in recent years, bringing the importance of testing-related challenges into the limelight. When generating test data for software testing, the test data must be able to unearth potential software defects, while the test adequacy criterion guarantees the quality of test cases. However, optimizing test data during software testing can improve software reliability. Recently, population-based metaheuristic search techniques (e.g., evolutionary testing) have been utilized in software testing. In this study, we propose and implement a method that utilizes a genetic algorithm to optimize test data for robot testing. Due to its advantages over traditional testing methods, several businesses have recently started using robot-automated testing systems for various applications. We implement a Robot Framework (R.F.) where we receive the data generated by a genetic algorithm. Furthermore, this generated data then acts as a request body for R.F. to test the fitness values and use the generated data as our necessary data sets.