To browse Academia. The printing of the book was funded by a grant from the Kurt Vogel Foundation Munich. Marek Reformat Edmonton, Canada kindly provided the photograph used in the front matter. The algorithm assumes that the initial set of points is divided into subsets and overlapping regions between these subsets.
The algorithm starts by considering a subset and find the solution in that subset. The solution in the next subset is calculated by expanding the earlier solution for the current subset and this process continues until all the subsets are considered.
The algorithm is tested with points and the results prove that it considerably reduces the computational time. In addition, its quality is close to that of the best known heuristic algorithm. As one of the most popular software testing techniques, fuzzing can find a variety of weaknesses in a program, such as software bugs and vulnerabilities, by generating numerous test inputs.
Due to its effectiveness, fuzzing is regarded as a valuable bug hunting method. In this paper, we present an overview of fuzzing that concentrates on its general process, as well as classifications, followed by detailed discussion of the key obstacles and some stateof-the-art technologies which aim to overcome or mitigate these obstacles.
We further investigate and classify several widely used fuzzing tools. Our primary goal is to equip the stakeholder with a better understanding of fuzzing and the potential solutions for improving fuzzing methods in the spectrum of software testing and security.
