LAUSR

Bugs are one of the important barriers in the field of software development. Concurrent and frequent bugs are non-deterministic in nature and in the time of vulnerability testing. It is difficult to detect the dynamic bugs with a high representation of vulnerability that causes the damage to the software products. Existing vulnerability testing methods relied on the conventional static testing techniques of finding and fixing the bugs but it does not show a high ratio of they handle or require specific hardware support. It does not include in the clustering approach. To overcome the limitations in the existing techniques, this proposed methods Modified Particle Swarm Optimization (MPSO), Expectation Maximization (EM) Clustering and Variable Neighborhood search. The primary input dataset is preprocessed to obtain the relevant and irrelevant data partition and optimized dataset was given as input to the Modified Particle Swarm Optimization (MPSO) technique