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<p>Different test cases may be used to identify the bugs, but selecting and executing particular test cases in a particular sequence is complex. Test cases can be selected using various parameters (i.e. importance, complexity, and prospective effect over the applications and number of faults/errors revealed by a test case). However, estimation of the execution priority is another issue. This paper highlights the various solutions developed by researchers to achieve this goal. This paper also presents a machine learning approach based on dynamic prioritization of regression test cases. Comparison with existing machine learning and deep learning approaches shows that it outperforms in terms of higher Average Percentage of Fault detection ratio (81.24%), Accuracy (96%), Precision (0.98), Recall (0.77) and F1-score (0.86).</p>

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Dynamic prioritization of test cases for regression testing using machine learning

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Gajender Rao,
  • Deepak Nandal

摘要

Different test cases may be used to identify the bugs, but selecting and executing particular test cases in a particular sequence is complex. Test cases can be selected using various parameters (i.e. importance, complexity, and prospective effect over the applications and number of faults/errors revealed by a test case). However, estimation of the execution priority is another issue. This paper highlights the various solutions developed by researchers to achieve this goal. This paper also presents a machine learning approach based on dynamic prioritization of regression test cases. Comparison with existing machine learning and deep learning approaches shows that it outperforms in terms of higher Average Percentage of Fault detection ratio (81.24%), Accuracy (96%), Precision (0.98), Recall (0.77) and F1-score (0.86).