Poster: Efficient Iterative Deepening for Bounded Exhaustive Generation of Complex Structures

The time required to generate valid structurally complex inputs grows exponentially with the input size. It makes it hard to predict, for a given structure, the most feasible input size that is completely explorable within a time budget. Iterative deepening generates inputs of size n before those of size n+1 and eliminates guesswork to find such size. We build on Korat algorithm for structural test generation and present iKorat – an incremental algorithm for efficient iterative deepening. It avoids redundant work as opposed to naïve Korat-based iterative deepening by using information from smaller sizes, which is kept in a highly compact format.