Optimization of association rules using hybrid data mining technique

Software quality has been the important area of interest for decades now in the IT sector and software firms. Defect prediction gives the tester the pointers as to where the bugs will most likely be hidden in the software product. Identifying and reporting the defect probe areas is the main job of software defect prediction techniques. Early detection of software defects during Software Development Life Cycle could lead to a reduction in cost of development, time involved in further testing activities and rework effort post-production and maintenance phase, thus resulting in more reliable software. Software metrics can be used for developing the defect prediction models. Several data mining techniques can be applied on the available open-source software datasets. These datasets are extracted from software programs. Such datasets made publicly available by National Aeronautics and Space Administration for their various softwares have been extensively used in software engineering-related research activities. These datasets contain information on associated Software Metrics at module level. The proposed idea is a novel hybrid data mining technique consisting of Clustering and Modified Apriori Algorithm that results in improved efficiency and reliability of Software Defect Prediction. This technique works by reducing the number of association rules generated. The results are achieved by using interestingness measure called spread. The paper also does a comparative analysis of the results obtained from the novel technique with the existing hybrid technique of Clustering and Apriori.