Software behavior modeling is very important to analyze software system security. The goal of software behavior analysis is to obtain the semantic description and understanding of the software behavior. Software behavior modeling and testing are the key steps to achieve the goal. Therefore, this paper studies the software behavior model algorithm, GK-tail algorithm. GK-tail algorithm uses interaction trace to describe the software behavior, which is concerned interaction between the software components. However, GK-tail algorithm is not accurate. Because GK-tail algorithm uses Daikon analysis tool to obtain the predicates, which contain some errors. In order to improve the accuracy of the GK-tail algorithm in software behavior modeling, this paper improves the traditional GK-tail algorithm based on the extended finite automaton. The improved GK-tail algorithm utilizes the combination of Daikon and static (ESC/Java) tools to filter out some errors of the generated predicates. In addition, this paper designs and implements a prototype system of the improved GK-tail algorithm. The prototype system proves that the improved GK-tail algorithm can filter out errors in the second step of GK-tail algorithm effectively.

https://www.dpi-proceedings.com/index.php/dtcse/article/download/17484/16991

The Improvement of GK-tail Algorithm of Software Behavior Modeling

The behavior of M.L. Tsetlin's automaton with linear tactics in a fuzzy environment is considered. The behavior of such an automaton was previously studied in a random environment, where the classical apparatus of probabilistic Markov chains could be used. This work is based on two results that were previously obtained by the author, namely, a theory that generalizes Markov chains and the axiomatic description of operations with fuzzy evidence. The explicit formulas for describing the behavior of the automaton that were obtained in this paper suggest a deep analogy with the results of M.L. Tsetlin. In particular, it is shown that this automaton in a fuzzy environment also has the property of asymptotic optimality. As an application, we discuss the possibility of using this property to measure membership functions for values that are similar to singletons or point functions. It is concluded that the results that are obtained in this paper make it possible to construct an analogue of the statistics collection procedure for the field of fuzzy systems.

V. L. Stefanyuk

Papers

The behavior of a finite-state automaton in a fuzzy environment: Theory and applications