Similarity Assessment of Program Samples Based on Theory of Fuzzy

Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The start-of-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted during compile time or performance counters collected when running the program. According to the program features, similarity values are calculated to assess the similar degree. In this paper, we introduce a novel way of assessing the similarity of two program samples using Theory of Fuzzy. We firstly calculate the Euclidean Distance of two different program samples as the input, and then assess the overall similarity degree as well as respective similarity degree, using corresponding Fuzzy Function. The graph-based characterization technique is cited, which shows great advantages than state-of-the-art characterization techniques found in the literature. By using the sequences predicted to be the best by the graph-based model, we compare the known sequences with unknown ones, and calculate the similarity between them, then obtain the similarity degree. Using these degrees, we are directed to make better determination to cluster or classify.