StateFusion Methodology Dataset

Published: 30 April 2024| Version 1 | DOI: 10.17632/7yd92xf9ck.1
Bekim Fetaji


The StateFusion Methodology Dataset is designed to facilitate and validate the effectiveness of the StateFusion methodology, a state reduction technique for pushdown automata (PDA). This dataset includes a comprehensive collection of PDA configurations, each characterized by an initial number of states, input alphabet, stack alphabet, transition functions, and final states. Each entry in the dataset represents a distinct PDA before and after the application of the StateFusion methodology, detailing the original state configuration alongside the optimized state configuration obtained through merging states with identical transitions and final states. Key components of the dataset include: Initial States: The total number of states in the PDA before applying StateFusion. Reduced States: The total number of states in the PDA after optimization. Input Alphabet: The set of symbols that the automaton can read. Stack Alphabet: The set of symbols that can be stored on the stack. Transitions: Detailed lists of state transitions before and after the reduction, showing the input symbol, stack symbol, next state, and stack operations (push or pop). Final States: Lists of accepting states before and after the reduction. Computational Metrics: Data on computational time and memory usage before and after the application of StateFusion, illustrating the efficiency gains. The dataset is intended for use by researchers and practitioners in the fields of computer science and computational linguistics to analyze, benchmark, and further develop state reduction techniques for automata. It provides a valuable resource for empirical studies on the impact of state reduction on automata performance, facilitating advancements in automata theory and its applications in parsing, language recognition, and beyond.



University Clinical Centre Mother Teresa Skopje, Mother Teresa Womens' University


Automata Theory