Automatic Grammar-Based Test Generation

In this paper, we present an automatic grammar-based test generation approach which takes a symbolic grammar as input, requires zero control input from users, and produces well-distributed test cases. Our approach utilizes a novel dynamic stochastic model where each variable is associated with a tuple of probability distributions, which are dynamically adjusted along the derivation. The adjustment is based on a tabling strategy to keep track of the recursion of each grammar variable. We further present a test generation coverage tree illustrating the distribution of generated test cases and their detailed derivations, more importantly, it provides various implicit balance control mechanisms. We implemented this approach in a Java-based system, named Gena. Experimental results demonstrate the effectiveness of our test generation approach and show the balanced distribution of generated test cases over grammatical structures.