DeepCrash: Deep Metric Learning for Crash Bucketing Based on Stack Trace
Liu Chao, Xie Qiaoluan, Li Yong, Xu Yang, and Choi Hyun-Deok
(SAP Labs, China; SAP Labs, South Korea)
Some software projects collect vast crash reports from testing and end users, then organize them in groups to efficiently fix bugs. This task is crash report bucketing. In particular, a high precision and fast speed crash similarity measurement approach is the critical constraint for large-scale crash bucketing. In this paper, we propose a deep learning-based crash bucketing method which maps stack trace to feature vectors and groups these feature vectors into buckets. First, we develop a frame tokenization method for stack trace, called frame2vec, to extract frame representations based on frame segmentation. Second, we propose a deep metric model to map the sequential stack trace representations into feature vectors whose similarity can represent the similarity of crashes. Third, a clustering algorithm is used to rapidly group similar feature vectors into same buckets to get the final result. Additionally, we evaluate our approach with the other seven competing methods on both private and public data sets. The results reveal that our method can speed up clustering and maintain high competitive precision.
@InProceedings{MaLTeSQuE22p29,
author = {Liu Chao and Xie Qiaoluan and Li Yong and Xu Yang and Choi Hyun-Deok},
title = {DeepCrash: Deep Metric Learning for Crash Bucketing Based on Stack Trace},
booktitle = {Proc.\ MaLTeSQuE},
publisher = {ACM},
pages = {29--34},
doi = {10.1145/3549034.3561179},
year = {2022},
}
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