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

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


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.

TitelThe 2022 Mining Software Repositories Conference : MSR 2022, Proceedings; 18-20 May 2022, Virtual; 23-24 May 2022, Pittsburgh, Pennsylvania
Anzahl der Seiten12
ErscheinungsortNew York
VerlagInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781665452106
ISBN (elektronisch)978-1-4503-9303-4
PublikationsstatusErschienen - 23.05.2022
Veranstaltung19th International Conference on Mining Software Repositories - MSR 2022 - Pittsburgh, USA / Vereinigte Staaten
Dauer: 23.05.202224.05.2022
Konferenznummer: 19

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