S3M: Siamese Stack (Trace) Similarity Measure

• URL: http://arxiv.org/abs/2103.10526v1
• Date: Thu, 18 Mar 2021 21:10:41 GMT
• Title: S3M: Siamese Stack (Trace) Similarity Measure
• Authors: Aleksandr Khvorov, Roman Vasiliev, George Chernishev, Irving Muller Rodrigues, Dmitrij Koznov, Nikita Povarov
• Abstract summary: We present S3M -- the first approach to computing stack trace similarity based on deep learning. It is based on a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset.
• Score: 55.58269472099399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application's bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information. In this paper, we present S3M ("extreme") -- the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.

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