Advancing Software Monitoring : An Industry Survey on ML-Driven Alert Management Strategies
(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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- author
- Hrusto, Adha
LU
; Runeson, Per LU
; Engström, Emelie LU
and Ohlsson, Magnus C LU
- organization
-
- LTH Profile Area: AI and Digitalization
- Software Engineering Research Group
- Department of Computer Science
- LU Profile Area: Natural and Artificial Cognition
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- Information and Communications Engineering (M.Sc.Eng.)
- LTH School of Engineering in Helsingborg
- publishing date
- 2024
- 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}}, }