Improving random test sets using the diversity oriented test data generation

Abstract

We present a measure that characterizes the diversity of a test set from the perspective of the input domain of the program under test. By using a metaheuristic algorithm, randomly generated test sets (RTS) are evolved towards Diversity Oriented Test Sets (DOTS), which thoroughly cover the input domain. DOTS are evaluated using a Monte Carlo simulation to assess how testing factors influence their effectiveness and also by the values of data flow coverage and mutation scores attained on simple programs. Results provide understanding on possible gains of using DOTS and on circumstances where RTS can be more effective.