Learning to prioritize defect reports in large scale validation pipelines for immersive software
DOI:
https://doi.org/10.66238/7xartf34Keywords:
software validation, defect prioritization, machine learning, software reliabilityAbstract
Large-scale validation pipelines for immersive software platforms produce thousands of defect reports during continuous integration and system testing. Efficiently prioritizing these reports is critical for reducing debugging time and improving software reliability. This study proposes a data-driven approach for prioritizing defect reports in large-scale validation pipelines for immersive systems. The proposed method integrates natural language processing of defect descriptions, runtime log features, and historical resolution metadata into a unified ranking model. A gradient-boosted learning framework was developed to predict defect severity and resolution urgency. The model was trained using 186,904 defect reports collected from continuous testing pipelines of XR software systems, including rendering failures, synchronization errors, and hardware interaction issues. Experimental evaluation shows that the proposed approach improves the identification rate of critical defects in the top-50 ranked reports by 31.2% compared with rule-based prioritization methods. In addition, average defect triage time was reduced by 35.4%, while duplicate report suppression improved by 27%. These results demonstrate that machine learning–based prioritization can significantly improve defect management efficiency in immersive software development environments.
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Copyright (c) 2026 Minho Kim, Jisoo Park, Hyunwoo Lee (Author)
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