Similarity in languages and programs

  • Authors:

  • Cewei Cui;Zhe Dang;Thomas R. Fischer;Oscar H. Ibarra

  • Affiliations:

  • School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA;Department of Computer Science, University of California, Santa Barbara, CA 93106, USA

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2013

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Abstract

We use an information-theoretic notion, namely, (Shannon) information rate, to generalize common syntactic similarity metrics (like Hamming distance and longest common subsequences) between strings to ones between languages. We show that the similarity metrics between two regular languages are computable. We further study self-similarity of a regular language under various similarity metrics. As far as semantic similarity is concerned, we study the amplitude of an automaton, which intuitively characterizes how much a typical execution of the automaton fluctuates. Finally, we investigate, through experiments, how to measure similarity between two real-world programs using Lempel-Ziv compression on the runs at the assembly level.