Framework for Software Testing Data Using Correlation Coefficient and Apriori Algorithm

Abstract

In this modern era of science and technology, the use of software and computer-aided programs has increased very rapidly. With the increase in use, the data and size of the computer software’s are also increased. Due to which the collection of large amounts of software testing data to support the software development and maintenance process has become difficult. With the development of the software, there is a need to assure the quality of the software. Software testing is the only solution to find the quality of the software and there is a need to find the defects in the software before delivering it to the clients. Almost 50% of the projects failed due to low quality and poor software testing. So, based on this problem we realized the need to use the latest data mining techniques for predicting defects in software. In this research study, we used Data Mining (DM) techniques to predict defects from the software testing data. With the help of data mining techniques, we can improve the reliability and quality of the software. First, we have identified some available software testing datasets and selected data based on the parameters and requirements of the research study. For this purpose, we have explored related studies in the Literature Review (LR) and identified some defect prediction datasets & techniques. Based on the literature review, we have found different defect prediction techniques and chose the best one for designing and implementing research methodology. After data selection, we found the correlation between the different parameters of the software testing dataset using the correlation analysis. Further, applied data cleaning & transformation for preprocessed data. Processed data contains on the continuous data, so we transformed data into discrete data while using clustering (grouping) techniques. Then we implemented Apriori algorithm under Association Rule Mining (ARM) technique for predicting defects in software testing data. Apriori algorithm provided the supports and confidence in multiple iterations, and we got more accurate results. This proposed framework is based on Market Basket Analysis (MBA) and found the most frequent defects while using Association Rule Mining.