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An incremental verification framework for component-based software systems

Published: 17 June 2013 Publication History

Abstract

We present a tool-supported framework for the efficient reverification of component-based software systems after changes such as additions, removals or modifications of components. The incremental verification engine at the core of our INcremental VErification STrategy (INVEST) framework uses high-level algebraic representations of component-based systems to identify and execute the minimal set of component-wise reverification steps after a system change. The generality of the INVEST engine allows its integration with existing assume-guarantee verification paradigms. We illustrate this integration for an existing technique for the assume-guarantee verification of probabilistic systems. The resulting instance of the INVEST framework can reverify probabilistic safety properties of a cloud-deployed software system in a fraction of the time required by compositional assume-guarantee verification alone.

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cover image ACM Conferences
CBSE '13: Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
June 2013
200 pages
ISBN:9781450321228
DOI:10.1145/2465449
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 June 2013

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Author Tags

  1. domain-specific languages
  2. incremental verification
  3. probabilistic assume-guarantee verification

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CBSE '13 Paper Acceptance Rate 20 of 43 submissions, 47%;
Overall Acceptance Rate 55 of 147 submissions, 37%

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