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Vamos: Middleware for Best-Effort Third-Party Monitoring

dc.contributor.authorChalupa, Mareken
dc.contributor.authorMuehlboeck, Fabianen
dc.contributor.authorLei, Stefanie Muroyaen
dc.contributor.authorHenzinger, Thomas A.en
dc.date.accessioned2026-05-14T14:40:46Z
dc.date.available2026-05-14T14:40:46Z
dc.date.issued2023en
dc.description.abstractAs the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both. We present a middleware framework, Vamos, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of Vamos are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, Vamos combines aspects of event broker and event recognition systems with aspects of stream processing systems. We implemented a prototype toolchain for Vamos and conducted experiments including a case study of monitoring for data races. The results indicate that Vamos enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch.en
dc.description.sponsorshipAcknowledgements This work was supported in part by the ERC-2020-AdG 101020093. The authors would like to thank the anonymous FASE reviewers for their valuable feedback and suggestions.en
dc.description.statusPeer-revieweden
dc.format.extent22en
dc.identifier.isbn9783031308253en
dc.identifier.issn0302-9743en
dc.identifier.scopus85161384180en
dc.identifier.urihttps://hdl.handle.net/1885/733809100
dc.language.isoenen
dc.publisherSpringer Science+Business Media B.V.en
dc.relation.ispartofFundamental Approaches to Software Engineering - 26th International Conference, FASE 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, Proceedingsen
dc.relation.ispartofseries26th International Conference on Fundamental Approaches to Software Engineering, FASE 2023, held as part of the 26th European Joint Conferences on Theory and Practice of Software, ETAPS 2023en
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.rightsPublisher Copyright: © 2023, The Author(s).en
dc.titleVamos: Middleware for Best-Effort Third-Party Monitoringen
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage281en
local.bibliographicCitation.startpage260en
local.contributor.affiliationChalupa, Marek; Institute of Science and Technology Austriaen
local.contributor.affiliationMuehlboeck, Fabian; Institute of Science and Technology Austriaen
local.contributor.affiliationLei, Stefanie Muroya; Institute of Science and Technology Austriaen
local.contributor.affiliationHenzinger, Thomas A.; Institute of Science and Technology Austriaen
local.identifier.doi10.1007/978-3-031-30826-0_15en
local.identifier.essn1611-3349en
local.identifier.pure3faf3759-64e4-4246-bc2e-55b7649ba444en
local.type.statusPublisheden

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