RUNMORE

Run-time Model Projections for Software Failure Prediction

Coordinatore	UNIVERSITA DELLA SVIZZERA ITALIANA    more  Organization address address: VIA LAMBERTENGHI 10 A city: LUGANO postcode: 6904 contact info Titolo: Ms. Nome: Paola Cognome: Colferai Email: send email Telefono: +41 58 666 48 18 Fax: +41 58 666 46 47
Nazionalità Coordinatore	Switzerland [CH]
Totale costo	184˙709 €
EC contributo	184˙709 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2011-IEF
Funding Scheme	MC-IEF
Anno di inizio	2013
Periodo (anno-mese-giorno)	2013-01-01   -   2014-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITA DELLA SVIZZERA ITALIANA  Organization address address: VIA LAMBERTENGHI 10 A city: LUGANO postcode: 6904 contact info Titolo: Ms. Nome: Paola Cognome: Colferai Email: send email Telefono: +41 58 666 48 18 Fax: +41 58 666 46 47	CH (LUGANO)	coordinator	184˙709.40

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 Obiettivo del progetto (Objective)

'The scope of this project is to contribute to the development of conceptual foundations, engineering techniques, and computing infrastructure for the systematic development of dynamically adaptive software systems. Current Software Engineering aims at designing self-adaptive systems which are able to react and reconfigure themselves minimizing human intervention and ideally guaranteeing a lifelong requirement fulfillment. Current software engineering paradigms, systems do not anticipate events which may lead to failures, but only react accordingly to them. The RunMore project introduces the novel concept of Run-Time Model Projection for Failure Prediction. By this we mean the ability of a software system at run-time to automatically forecast potentially dangerous events by reasoning on models which represent the expected future behavior of the system (i.e., models projections) and thus work around predicted failures before their occurrence. This approach empowers self-adaptation capabilities of software systems obtaining an increased degree of dependability and availability. The project focuses on software self-adaptation in terms of performance and reliability and relies on statistical algorithms to predict the future behavior of the system and run-time model-checking to verify such behavior with respect to desired requirements.'