NDLI: The unreasonable effectiveness of traditional information retrieval in crash report deduplication

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The unreasonable effectiveness of traditional information retrieval in crash report deduplication

Content Provider	ACM Digital Library
Author	Hindle, Abram Santos, Eddie Antonio Campbell, Joshua Charles
Abstract	Organizations like Mozilla, Microsoft, and Apple are flooded with thousands of automated crash reports per day. Although crash reports contain valuable information for debugging, there are often too many for developers to examine individually. Therefore, in industry, crash reports are often automatically grouped together in buckets. Ubuntu's repository contains crashes from hundreds of software systems available with Ubuntu. A variety of crash report bucketing methods are evaluated using data collected by Ubuntu's Apport automated crash reporting system. The trade-off between precision and recall of numerous scalable crash deduplication techniques is explored. A set of criteria that a crash deduplication method must meet is presented and several methods that meet these criteria are evaluated on a new dataset. The evaluations presented in this paper show that using off-the-shelf information retrieval techniques, that were not designed to be used with crash reports, outperform other techniques which are specifically designed for the task of crash bucketing at realistic industrial scales. This research indicates that automated crash bucketing still has a lot of room for improvement, especially in terms of identifier tokenization.
Starting Page	269
Ending Page	280
Page Count	12
File Format	PDF
ISBN	9781450341868
DOI	10.1145/2901739.2901766
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Automatic crash reporting Duplicate crash report Contextual information Deduplication Information retrieval Call stack trace Duplicate bug reports Free/open source software Software engineering
Content Type	Text
Resource Type	Article

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