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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 is 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. In this paper we used data engineering techniques to discover the information from the software testing data. It will help to increase the reliability and quality of the product. First, we find the correlation between the different parameters of the software testing data set using the correlation analysis. Then we defined the analysis objects and applied the Apriori algorithm-based association rule algorithm model in multiple iterations to get more accurate results. Finally, the associations and relations obtained by analysis are shown.

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Framework for Efficient Identification of Features Associated with Software Defects Using Spearman Correlation and Apriori Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Muhammad Umar Sajjad,
  • Muhammad Qasim Riaz,
  • Usman Qamar,
  • Muhammad Danish Riaz,
  • Muhammad Talha Imtiaz,
  • Muhammad Arif

摘要

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 is 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. In this paper we used data engineering techniques to discover the information from the software testing data. It will help to increase the reliability and quality of the product. First, we find the correlation between the different parameters of the software testing data set using the correlation analysis. Then we defined the analysis objects and applied the Apriori algorithm-based association rule algorithm model in multiple iterations to get more accurate results. Finally, the associations and relations obtained by analysis are shown.