Footprints of fitness functions in search-based software testing

© 2019 ACM. 978-1-4503-6111-8/19/07. . . $15.00 Testing is technically and economically crucial for ensuring software quality. One of the most challenging testing tasks is to create test suites that will reveal potential defects in software. However, as the size and complexity of software systems increase, the task becomes more labour-intensive and manual test data generation becomes infeasible. To address this issue, researchers have proposed different approaches to automate the process of generating test data using search techniques; an area that is known as Search-Based Software Testing (SBST). SBST methods require a fitness function to guide the search to promising areas of the solution space. Over the years, a plethora of fitness functions have been proposed. Some methods use control information, others focus on goals. Deciding on what fitness function to use is not easy, as it depends on the software system under test. This work investigates the impact of software features on the effectiveness of different fitness functions. We propose the Mapping the Effectiveness of Test Automation (META) Framework which analyses the footprint of different fitness functions and creates a decision tree that enables the selection of the appropriate function based on software features.