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DeepAnalyze: Learning to Localize Crashes at Scale by Shetty, Manish; Bansal, Chetan; Nath, Suman; Bowles, Sean; Wang, Henry; Arman, Ozgur; Ahari, Siamak is a scholarly article available to read on EtoBox.

What is DeepAnalyze: Learning to Localize Crashes at Scale about?

Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to localize blamed frames causing the crashes. As new applications and features are routinely introduced and existing applications are run under new environments, developing new rules and maintaining existing ones become extremely challenging. We propose a data-driven solution to address the problem. We start with the first large-scale empirical study of 362K crashes and their blamed methods reported to WER by tens of thousands of applications running in the field. The analysis provides valuable insights on where and how the crashes happen and what methods to blame for the crashes. These insights enable us to develop DeepAnalyze, a novel multi-task sequence labeling approach for identifying blamed frames in stack traces. We evaluate our model with over a million real-world crashes from four popular Microsoft applications and show that DeepAnalyze, trained with crashes fro

Author

Shetty, Manish; Bansal, Chetan; Nath, Suman; Bowles, Sean; Wang, Henry; Arman, Ozgur; Ahari, Siamak

Published

2021

Language

EN