Abstract: A symbolic execution device, a symbolic execution method, and a probability distribution update method for symbolic execution are provided. The symbolic execution device includes a storage unit for storing at least one of a first probability distribution and a second probability distribution and a processor for sampling and obtaining a weight vector from the first probability distribution, for sampling and obtaining a removal rate from the second probability distribution, for adding the weight vector to at least one feature vector obtained from at least one candidate state to obtain a score for each of the at least one candidate state, and for selecting a state to be removed from among the at least one candidate state by using the score of each of the at least one candidate state and the removal rate.

Abstract: Provided is a method for automatically generating a search heuristic that is optimal for a test subject program and a method of concolic testing that uses a parameterized search heuristic to yield a consistent test performance for any program.

Abstract: Provided is a method for automatically generating a search heuristic that is optimal for a test subject program and a method of concolic testing that uses a parameterized search heuristic to yield a consistent test performance for any program.

Abstract: Provided is a method for automatically generating a search heuristic that is optimal for a test subject program and a method of concolic testing that uses a parameterized search heuristic to yield a consistent test performance for any program.

Abstract: A method of generating a search heuristic for concolic testing includes: (a) generating feature vectors corresponding to branches included in the subject program and initializing sample spaces; (b) randomly generating first parameter vectors that contain components selected from the sample spaces; (c) selecting second parameter vectors based on a first branch coverage of each first parameter vector for the subject program; (d) reducing the sample spaces based on an average branch coverage of each second parameter vector for the subject program; (e) repeating steps (b) to (d) such that the average branch coverage of some of the second parameter vectors converges to a maximum; and (f) when the average branch coverage converges to a maximum, outputting an optimal parameter vector corresponding to the maximum average branch coverage, where the components of the first parameter vectors are selected from the sample spaces reduces in step (d) when step (b) is repeated.