Black-Box String Test Case Generation through a Multi-Objective Optimization

String test cases are required by many real-world applications to identify defects and security risks. Random Testing (RT) is a low cost and easy to implement testing approach to generate strings. However, its effectiveness is not satisfactory. In this research, black-box string test case generation methods are investigated. Two objective functions are introduced to produce effective test cases. The diversity of the test cases is the first objective, where it can be measured through string distance functions. The second objective is guiding the string length distribution into a Benford distribution based on the hypothesis that the population of strings is right-skewed within its range. When both objectives are applied via a multi-objective optimization algorithm, superior string test sets are produced. An empirical study is performed with several real-world programs indicating that the generated string test cases outperform test cases generated by other methods.