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Advancing Software Monitoring : An Industry Survey on ML-Driven Alert Management Strategies

Hrusto, Adha LU orcid ; Runeson, Per LU orcid ; Engström, Emelie LU orcid and Ohlsson, Magnus C LU (2024) 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024 p.435-442
Abstract

With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring... (More)

With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring systems. Key findings revealed a growing but still limited reliance on ML to intelligently filter raw monitoring data, prioritize issues, and respond to system alerts, thereby improving operational efficiency and system reliability. The paper also discusses the barriers to adopting ML-based solutions and provides insights into the future direction of software monitoring.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
alert management, anomaly detection, machine learning, monitoring
host publication
Proceedings - 2024 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024
edition
2024
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024
conference location
Paris, France
conference dates
2024-08-28 - 2024-08-30
external identifiers
  • scopus:85218623260
ISBN
9798350380262
DOI
10.1109/SEAA64295.2024.00073
project
Continuous system testing using autonomous monitors
Continuous Software Engineering
language
English
LU publication?
yes
id
0e575948-fc6a-40b4-af3b-d8c50383996e
date added to LUP
2024-05-28 09:22:45
date last changed
2025-04-04 15:19:13
@inproceedings{0e575948-fc6a-40b4-af3b-d8c50383996e,
  abstract     = {{<p>With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring systems. Key findings revealed a growing but still limited reliance on ML to intelligently filter raw monitoring data, prioritize issues, and respond to system alerts, thereby improving operational efficiency and system reliability. The paper also discusses the barriers to adopting ML-based solutions and provides insights into the future direction of software monitoring.</p>}},
  author       = {{Hrusto, Adha and Runeson, Per and Engström, Emelie and Ohlsson, Magnus C}},
  booktitle    = {{Proceedings - 2024 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024}},
  isbn         = {{9798350380262}},
  keywords     = {{alert management; anomaly detection; machine learning; monitoring}},
  language     = {{eng}},
  pages        = {{435--442}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Advancing Software Monitoring : An Industry Survey on ML-Driven Alert Management Strategies}},
  url          = {{https://lup.lub.lu.se/search/files/187867605/IEEE_SEAA_Survey.pdf}},
  doi          = {{10.1109/SEAA64295.2024.00073}},
  year         = {{2024}},
}