TY - CHAP

T1 - FaST: A linear time stack trace alignment heuristic for crash report deduplication

AU - Rodrigues, Irving Muller

AU - Aloise, Daniel

AU - Fernandes, Eraldo Rezende

N1 - Conference code: 19

PY - 2022/5/23

Y1 - 2022/5/23

N2 - 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.

AB - 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.

KW - Automatic Crash Reporting

KW - Crash Report Deduplication

KW - Duplicate Crash Report

KW - Duplicate Crash Report Detection

KW - Stack Trace Similarity

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85134075597&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/9796190

UR - https://www.proceedings.com/64472.html

U2 - 10.1145/3524842.3527951

DO - 10.1145/3524842.3527951

M3 - Article in conference proceedings

AN - SCOPUS:85134075597

SN - 9781665452106

T3 - Proceedings - IEEE/ACM International Conference on Mining Software Repositories

SP - 549

EP - 560

BT - The 2022 Mining Software Repositories Conference

PB - Institute of Electrical and Electronics Engineers Inc.

CY - New York

T2 - 19th International Conference on Mining Software Repositories - MSR 2022

Y2 - 23 May 2022 through 24 May 2022

ER -