Biblio

Detecting software security vulnerabilities and distinguishing vulnerable from non-vulnerable code is anything but simple. Most of the time, vulnerabilities remain undisclosed until they are exposed, for instance, by an attack during the software operational phase. Software metrics are widely-used indicators of software quality, but the question is whether they can be used to distinguish vulnerable software units from the non-vulnerable ones during development. In this paper, we perform an exploratory study on software metrics, their interdependency, and their relation with security vulnerabilities. We aim at understanding: i) the correlation between software architectural characteristics, represented in the form of software metrics, and the number of vulnerabilities; and ii) which are the most informative and discriminative metrics that allow identifying vulnerable units of code. To achieve these goals, we use, respectively, correlation coefficients and heuristic search techniques. Our analysis is carried out on a dataset that includes software metrics and reported security vulnerabilities, exposed by security attacks, for all functions, classes, and files of five widely used projects. Results show: i) a strong correlation between several project-level metrics and the number of vulnerabilities, ii) the possibility of using a group of metrics, at both file and function levels, to distinguish vulnerable and non-vulnerable code with a high level of accuracy.

Metaheuristic search technique is one of the advance approach when compared with traditional heuristic search technique. To select one option among different alternatives is not hard to get but really hard is give assurance that being cost effective. This hard problem is solved by the meta-heuristic search technique with the help of fitness function. Fitness function is a crucial metrics or a measure which helps in deciding which solution is optimal to choose from available set of test sets. This paper discusses hill climbing, simulated annealing, tabu search, genetic algorithm and particle swarm optimization techniques in detail explaining with the help of the algorithm. If metaheuristic search techniques combine some of the security testing methods, it would result in better searching technique as well as secure too. This paper primarily focusses on the metaheuristic search techniques.