Intraprocedural Dataflow Analysis for Software Product Lines

Brabrand, Claus; Ribeiro, Márcio; Tolêdo, Társis; Winther, Johnni; Borba, Paulo

doi:10.1007/978-3-642-36964-3_3

Cited by 40 publications

(96 citation statements)

References 36 publications

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“…Instead of performing |K| analyses, we now perform one analysis on |K|-sized tuples-afterall: |K|×1 = 1×|K|. Note that, even so, empirical evidence suggests that the latter might be faster than the former because of caching effects [27]. At the lifted analysis level, however, a lot of improvements are possible.…”

Section: Efficient Lifted Analysesmentioning

confidence: 97%

Systematic derivation of correct variability-aware program analyses

Midtgaard

Dimovski

Brabrand

et al. 2015

Science of Computer Programming

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interpretation A recent line of work lifts particular verification and analysis methods to Software Product Lines (SPL). In an effort to generalize such case-by-case approaches, we develop a systematic methodology for lifting single-program analyses to SPLs using abstract interpretation. Abstract interpretation is a classical framework for deriving static analyses in a compositional, step-by-step manner. We show how to take an analysis expressed as an abstract interpretation and lift each of the abstract interpretation steps to a family of programs (SPL). This includes schemes for lifting domain types, and combinators for lifting analyses and Galois connections. We prove that for analyses developed using our method, the soundness of lifting follows by construction. The resulting variational abstract interpretation is a conceptual framework for understanding, deriving, and validating static analyses for SPLs. Then we show how to derive the corresponding variational dataflow equations for an example static analysis, a constant propagation analysis. We also describe how to approximate variability by applying variability-aware abstractions to SPL analysis. Finally, we discuss how to efficiently implement our method and present some evaluation results.

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Section: Efficient Lifted Analysesmentioning

confidence: 97%

Systematic derivation of correct variability-aware program analyses

Midtgaard

Dimovski

Brabrand

et al. 2015

Science of Computer Programming

Self Cite

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“…The family-based strategy has been applied to several analysis techniques, including type checking [3,14,18,29], static analysis [9,10,21], model checking [4,7,11,19], performance measurement [26], and deductive verification [31]. The feature-based strategy has been used before for type checking [2,8] and verification [20] of product lines.…”

Section: Related Workmentioning

confidence: 99%

A comparison of product-based, feature-based, and family-based type checking

et al. 2013

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Analyzing software product lines is difficult, due to their inherent variability. In the past, several strategies for product-line analysis have been proposed, in particular, product-based, feature-based, and family-based strategies. Despite recent attempts to conceptually and empirically compare different strategies, there is no work that empirically compares all of the three strategies in a controlled setting. We close this gap by extending a compiler for featureoriented programming with support for product-based, featurebased, and family-based type checking. We present and discuss the results of a comparative performance evaluation that we conducted on a set of 12 feature-oriented, JAVA-based product lines. Most notably, we found that the family-based strategy is superior for all subject product lines: it is substantially faster, it detects all kinds of errors, and provides the most detailed information about them.

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“…The key idea is to build the refactoring engine on a canonical representation of the configurable system that includes all variability [30], and to use variability-aware static analysis [13], [10], [31], [37] and transformation to implement refactorings.…”

Section: Introductionmentioning

confidence: 99%

Morpheus: Variability-Aware Refactoring in the Wild

Liebig

Apel

Janker

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Today, many software systems are configurable with conditional compilation. Just like any software system, configurable systems need to be refactored in their evolution, but their inherent variability induces an additional dimension of complexity that is not addressed well by current academic and industrial refactoring engines. To improve the state of the art, we propose a variability-aware refactoring approach that relies on a canonical variability representation and recent work on variability-aware analysis. The goal is to preserve the behavior of all variants of a configurable system, without compromising general applicability and scalability. To demonstrate practicality, we developed MORPHEUS, a sound, variability-aware refactoring engine for C code with preprocessor directives. We applied MORPHEUS to three substantial real-world systems (BUSYBOX, OPENSSL, and SQLITE) showing that it scales reasonably well, despite of its heavy reliance on satisfiability solvers. By extending a standard approach of testing refactoring engines with support for variability, we provide evidence for the correctness of the refactorings implemented.

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Intraprocedural Dataflow Analysis for Software Product Lines

Cited by 40 publications

References 36 publications

Systematic derivation of correct variability-aware program analyses

Systematic derivation of correct variability-aware program analyses

A comparison of product-based, feature-based, and family-based type checking

Morpheus: Variability-Aware Refactoring in the Wild

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