Incrementally predictive runtime verification.
Saved in:
| Title: | Incrementally predictive runtime verification. |
|---|---|
| Authors: | Ferrando, Angelo1 (AUTHOR), Delzanno, Giorgio1 (AUTHOR) |
| Source: | Journal of Logic & Computation. Jun2023, Vol. 33 Issue 4, p796-817. 22p. |
| Subjects: | Process mining, Run time systems (Computer science), Workflow |
| Abstract: | Runtime verification is a lightweight formal verification technique used to verify the runtime behaviour of software (resp. hardware) systems. Given a formal property, one or more monitors are synthesized to verify the latter against a system execution. A monitor can only conclude the violation of a property when it observes such a violation. Unfortunately, in safety-critical scenarios, this might happen too late for the system to react properly. In such scenarios, it is advised to use predictive runtime verification, where monitors are capable of anticipating (by using a model of the system) future events before actually observing them. In this work, instead of assuming such a model is given, we describe a runtime verification workflow where the model is learnt and incrementally refined by using process mining techniques. We present the approach and the resulting prototype tool. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 164219356 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Incrementally predictive runtime verification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ferrando%2C+Angelo%22">Ferrando, Angelo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Delzanno%2C+Giorgio%22">Delzanno, Giorgio</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Logic+%26+Computation%22">Journal of Logic & Computation</searchLink>. Jun2023, Vol. 33 Issue 4, p796-817. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Process+mining%22">Process mining</searchLink><br /><searchLink fieldCode="DE" term="%22Run+time+systems+%28Computer+science%29%22">Run time systems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Runtime verification is a lightweight formal verification technique used to verify the runtime behaviour of software (resp. hardware) systems. Given a formal property, one or more monitors are synthesized to verify the latter against a system execution. A monitor can only conclude the violation of a property when it observes such a violation. Unfortunately, in safety-critical scenarios, this might happen too late for the system to react properly. In such scenarios, it is advised to use predictive runtime verification, where monitors are capable of anticipating (by using a model of the system) future events before actually observing them. In this work, instead of assuming such a model is given, we describe a runtime verification workflow where the model is learnt and incrementally refined by using process mining techniques. We present the approach and the resulting prototype tool. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=164219356 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/logcom/exad012 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 796 Subjects: – SubjectFull: Process mining Type: general – SubjectFull: Run time systems (Computer science) Type: general – SubjectFull: Workflow Type: general Titles: – TitleFull: Incrementally predictive runtime verification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ferrando, Angelo – PersonEntity: Name: NameFull: Delzanno, Giorgio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 0955792X Numbering: – Type: volume Value: 33 – Type: issue Value: 4 Titles: – TitleFull: Journal of Logic & Computation Type: main |
| ResultId | 1 |
