FaST: A linear time stack trace alignment heuristic for crash report deduplication

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


In software projects, applications are often monitored by systems that automatically identify crashes, collect their information into reports, and submit them to developers. Especially in popular applications, such systems tend to generate a large number of crash reports in which a significant portion of them are duplicate. Due to this high submission volume, in practice, the crash report deduplication is supported by devising automatic systems whose efficiency is a critical constraint. In this paper, we focus on improving deduplication system throughput by speeding up the stack trace comparison. In contrast to the state-of-the-art techniques, we propose FaST, a novel sequence alignment method that computes the similarity score between two stack traces in linear time. Our method independently aligns identical frames in two stack traces by means of a simple alignment heuristic. We evaluate FaST and five competing methods on four datasets from open-source projects using ranking and binary metrics. Despite its simplicity, FaST consistently achieves state-of-the-art performance regarding all metrics considered. Moreover, our experiments confirm that FaST is substantially more efficient than methods based on optimal sequence alignment.

Original languageEnglish
Title of host publicationThe 2022 Mining Software Repositories Conference : MSR 2022, Proceedings; 18-20 May 2022, Virtual; 23-24 May 2022, Pittsburgh, Pennsylvania
Number of pages12
Place of PublicationNew York
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date23.05.2022
ISBN (Print)9781665452106
ISBN (Electronic)978-1-4503-9303-4
Publication statusPublished - 23.05.2022
Event19th International Conference on Mining Software Repositories - MSR 2022 - Pittsburgh, United States
Duration: 23.05.202224.05.2022
Conference number: 19

Bibliographical note

Titel der Druckausgabe: 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR 2022)

Funding Information:
We would like to gratefully acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC), Ericsson, Ciena, and EffciOS for funding this project. Moreover, this research was enabled in part by the support provided by WestGrid (https://www. and Compute Canada (

Publisher Copyright:
© 2022 ACM.

    Research areas

  • Automatic Crash Reporting, Crash Report Deduplication, Duplicate Crash Report, Duplicate Crash Report Detection, Stack Trace Similarity
  • Business informatics