Regression Test Case Prioritization Approach Based on Deep Learning

Prioritizing test cases in regression testing can expedite the detection of code defects,save testing time and resources,and enhance testing efficiency.However,existing test case prioritization methods often fail to consider both code change information and test case execution history simultaneously,and they do not adequately account for differences in the length of test case execution history,resulting in poor prioritization outcomes.To address these issues,this paper introduces a deep learning-based approach for prioritizing regression test cases.Initially,it constructs classification models based on code change information and historical execution data separately.Subsequently,it identifies classes affected by code changes using inter-class relationship graphs and classifies test cases belonging to these classes,as well as those that have recently exposed defects.Finally,it employs classification models and heuristic sorting method to prioritize the test cases,followed by merging the sorted results through an iterative process.Experimental results on 6 projects selected from the preprocessed RTPTorrent dataset demonstrate that:1)in scenarios without time constraints,the proposed approach achieves impressive prioritization results across all projects,with an APFD of 0.972 on the cloudify project;2)under time-constrained conditions,the proposed approach outperforms popular existing prioritization methods in terms of NAPFD metrics.