TraceSim: A method for calculating stack trace similarity

Roman Vasiliev, Dmitrij Koznov, George Chernishev, Aleksandr Khvorov, Dmitry Luciv, Nikita Povarov

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Many contemporary software products have subsystems for automatic crash reporting. However, it is well-known that the same bug can produce slightly different reports. To manage this problem, reports are usually grouped, often manually by developers. Manual triaging, however, becomes infeasible for products that have large userbases, which is the reason for many different approaches to automating this task. Moreover, it is important to improve quality of triaging due to a large volume of reports that needs to be processed properly. Therefore, even a relatively small improvement could play a significant role in the overall accuracy of report bucketing. The majority of existing studies use some kind of a stack trace similarity metric, either based on information retrieval techniques or string matching methods. However, it should be stressed that the quality of triaging is still insufficient. In this paper, we describe TraceSim-a novel approach to this problem which combines TF-IDF, Levenshtein distance, and machine learning to construct a similarity metric. Our metric has been implemented inside an industrial-grade report triaging system. The evaluation on a manually labeled dataset shows significantly better results compared to baseline approaches.

Original languageEnglish
Title of host publicationMaLTeSQuE 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, Co-located with ESEC/FSE 2020
EditorsFoutse Khomh, Pasquale Salza, Gemma Catolino
PublisherAssociation for Computing Machinery
Pages25-30
Number of pages6
ISBN (Electronic)9781450381246
DOIs
StatePublished - 13 Nov 2020
Event4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, MaLTeSQuE 2020, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020 - Virtual, Online, United States
Duration: 13 Nov 2020 → …

Publication series

NameMaLTeSQuE 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, Co-located with ESEC/FSE 2020

Conference

Conference4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, MaLTeSQuE 2020, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020
CountryUnited States
CityVirtual, Online
Period13/11/20 → …

Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Safety, Risk, Reliability and Quality

Keywords

  • Automatic Crash Reporting
  • Automatic Problem Reporting Tools
  • Crash Report Deduplication
  • Crash Reports
  • Crash Stack
  • Deduplication
  • Duplicate Bug Report
  • Duplicate Crash Report
  • Information Retrieval
  • Software Engineering
  • Software Repositories
  • Stack Trace

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