Family-Based Modeling and Analysis for Probabilistic Systems - Featuring ProFeat

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Abstract

Feature-based formalisms provide an elegant way to specify families of systems that share a base functionality and differ in certain features. They can also facilitate an all-in-one analysis, where all systems of the family are analyzed at once on a single family model instead of one-by-one. This paper presents the basic concepts of the tool PROFEAT, which provides a guarded-command language for modeling families of probabilistic systems and an automatic translation of family models to the input language of the probabilistic model checker PRISM. This translational approach enables a family-based quantitative analysis with Prism. Besides modeling families of systems that differ in system parameters such as the number of identical processes or channel sizes, PROFEAT also provides special support for the modeling and analysis of (probabilistic) product lines with dynamic feature switches, multi-features and feature attributes. By means of several case studies we show how PROFEAT eases family-based modeling and compare the one-by-one and all-in-one analysis approach.

Details

Original languageEnglish
Title of host publicationFundamental Approaches to Software Engineering
EditorsPerdita Stevens, Andrzej Wąsowski
PublisherSpringer, Berlin [u. a.]
Pages287-304
Number of pages18
ISBN (print)978-3-662-49664-0
Publication statusPublished - 2016
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science, Volume 9633
ISSN0302-9743

Conference

Title19th International Conference on Fundamental Approaches to Software Engineering
Abbreviated titleFASE 2016
Conference number
Duration4 - 7 April 2016
Degree of recognitionInternational event
Location
CityEindhoven
CountryNetherlands

External IDs

Scopus 84961726965
ORCID /0000-0002-5321-9343/work/142236729

Keywords

Keywords

  • Family-Based Modeling and Analysis for Probabilistic Systems – Featuring ProFeat

Library keywords