Abstract
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
Highlights
Software Quality Assurance is used to ensure the quality of software in the field of software product development
It will improve the standard of the out coming Software Bugs are one of the important factors in STLC (Software Testing Life Cycle)
Variable Neighborhood Search T temp fori=0: Noddo forj=0: Noddo if end for end for end if Search neighborhood vulnerability a value to maximum vulnerability test cases with threshold vulnerability value used by the system and returns the maximum and data minimum values to predict to the final daataset
Summary
Software Quality Assurance is used to ensure the quality of software in the field of software product development It will improve the standard of the out coming Software Bugs are one of the important factors in STLC (Software Testing Life Cycle). Bug detection or prediction will be helpful to the software developers and testers. It includes the number of bugs, non-trivial bugs, number of major bugs, number of critical bugs, number of high priority bugs. The data mining is used to the clustering techno logies. They result in two algorithm execution on the software quality assurances testing and vulnerability detection on the work
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