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SPLLIFT: statically analyzing software product lines in minutes instead of years

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Published
  • Eric Bodden
  • Társis Tolêdo
  • Márcio Ribeiro
  • Claus Brabrand
  • Paulo Borba
  • Mira Mezini
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Publication date2013
Host publicationProceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation (PLDI 2013)
Place of PublicationNew York, NY, USA
PublisherACM
Pages355-364
Number of pages10
ISBN (print)978-1-4503-2014-6
<mark>Original language</mark>English
EventPLDI 2013 34th ACM SIGPLAN conference on Programming language design and implementation - Seattle, Wash., United States
Duration: 16/06/201322/06/2013

Conference

ConferencePLDI 2013 34th ACM SIGPLAN conference on Programming language design and implementation
Country/TerritoryUnited States
CitySeattle, Wash.
Period16/06/1322/06/13

Conference

ConferencePLDI 2013 34th ACM SIGPLAN conference on Programming language design and implementation
Country/TerritoryUnited States
CitySeattle, Wash.
Period16/06/1322/06/13

Abstract

A software product line (SPL) encodes a potentially large variety of software products as variants of some common code base. Up until now, re-using traditional static analyses for SPLs was virtually intractable, as it required programmers to generate and analyze all products individually. In this work, however, we show how an important class of existing inter-procedural static analyses can be transparently lifted to SPLs. Without requiring programmers to change a single line of code, our approach SPLLIFT automatically converts any analysis formulated for traditional programs within the popular IFDS framework for inter-procedural, finite, distributive, subset problems to an SPL-aware analysis formulated in the IDE framework, a well-known extension to IFDS. Using a full implementation based on Heros, Soot, CIDE and JavaBDD, we show that with SPLLIFT one can reuse IFDS-based analyses without changing a single line of code. Through experiments using three static analyses applied to four Java-based product lines, we were able to show that our approach produces correct results and outperforms the traditional approach by several orders of magnitude.