Variability-aware parsing in the presence of lexical macros and conditional compilation

Kästner, Christian; Giarrusso, Paolo G.; Rendel, Tillmann; Erdweg, Sebastian; Ostermann, Klaus; Berger, Thorsten

doi:10.1145/2076021.2048128

Cited by 65 publications

(90 citation statements)

References 47 publications

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“…The dominance of modular features, low scattering, and coarse-grained annotations mitigates the challenges imposed by the use of ifdef annotations on program comprehension (Favre 1997;Kästner and Apel 2009;Le et al 2011;Spencer and Collyer 1992) and on the potential of introducing bugs (Ernst et al 2002;Kästner et al 2011). While modularity is supported by the plugin architecture of the kernel, low scattering and coarse grain annotations appear to follow directly from coding guidelines related to ifdef use: 29 "Code cluttered with ifdefs is difficult to read and maintain.…”

Section: Kernel Evolution Principlesmentioning

confidence: 99%

“…Due to its complexity, publicly available source code, and practical appeal, researchers often study the Linux kernel to better understand practical issues arising from the maintenance of variant-rich software systems, subsequently deriving tool support that industry can directly benefit from Kästner et al 2011;Nadi et al 2014;Nadi and Holt 2012;She et al 2011;Tartler et al 2012). In our case, we are particularly interested in understanding how developers coevolve the kernel variability model, build files, and C source code.…”

Section: Introductionmentioning

confidence: 99%

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Coevolution of variability models and related software artifacts

et al. 2015

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Variant-rich software systems offer a large degree of customization, allowing users to configure the target system according to their preferences and needs. Facing high Empir Software Eng degrees of variability, these systems often employ variability models to explicitly capture user-configurable features (e.g., systems options) and the constraints they impose. The explicit representation of features allows them to be referenced in different variation points across different artifacts, enabling the latter to vary according to specific feature selections. In such settings, the evolution of variability models interplays with the evolution of related artifacts, requiring the two to evolve together, or coevolve. Interestingly, little is known about how such coevolution occurs in real-world systems, as existing research has focused mostly on variability evolution as it happens in variability models only. Furthermore, existing techniques supporting variability evolution are usually validated with randomly-generated variability models or evolution scenarios that do not stem from practice. As the community lacks a deep understanding of how variability evolution occurs in real-world systems and how it relates to the evolution of different kinds of software artifacts, it is not surprising that industry reports existing tools and solutions ineffective, as they do not handle the complexity found in practice. Attempting to mitigate this overall lack of knowledge and to support tool builders with insights on how variability models coevolve with other artifact types, we study a large and complex real-world variant-rich software system: the Linux kernel. Specifically, we extract variability-coevolution patterns capturing changes in the variability model of the Linux kernel with subsequent changes in Makefiles and C source code. From the analysis of the patterns, we report on findings concerning evolution principles found in the kernel, and we reveal deficiencies in existing tools and theory when handling changes captured by our patterns.

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Section: Kernel Evolution Principlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coevolution of variability models and related software artifacts

et al. 2015

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“…Kästner et al [34] show how languages with preprocessor syntax can be parsed and represented in syntax trees with variability, even if the preprocessor syntax is not properly nested in the main language syntax. Erwig and Walkingshaw [8] present the Choice Calculus, which can be seen as an elegant version of a preprocessor with a fixed and well defined semantics.…”

Section: Related Workmentioning

confidence: 99%

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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“…We plan to analyze several configurable software systems. We will first extract the constraints in each of the three spaces using existing tools (e.g., [4], [9]). We then plan to compare the constraints in the three spaces to find commonalities and overlaps, and identify common cases where the constraints in the variability model are reflected in the code or not.…”

Section: A Rq1: What Information Does Each Of the Three Spaces Provimentioning

confidence: 99%

“…In either case, the constraints enforced by these three spaces must be clear and consistent. Previous work has typically studied one of these spaces in isolation [3], [4] or in terms of how two of the spaces coevolve [5], [6]. However, there has not been an attempt to comprehensively study the variability of the three combined.…”

Section: Introductionmentioning

confidence: 99%

A study of variability spaces in open source software

Nadi¹

2013

2013 35th International Conference on Software Engineering (ICSE)

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Configurable software systems allow users to customize them according to their needs. Supporting such variability is commonly divided into three parts: configuration space, build space, and code space. In this research abstract, we describe our work in exploring what information these spaces contain in practice, and if this information is consistent. This involves investigating how these spaces work together to ensure that variability is correctly implemented, and to avoid any inconsistencies or anomalies. Our work identifies how variability is implemented in several configurable systems, and initially focuses on less studied parts such as the build system. Our goals include: 1) investigating what information each space provides, 2) quantifying the variability in the build system, 3) studying the effect of build system constraints on variability anomalies, and 4) analyzing how variability anomalies are introduced and fixed. Achieving these goals would help developers make informed decisions when designing variable software, and improve maintainability of existing configurable systems.

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Variability-aware parsing in the presence of lexical macros and conditional compilation

Cited by 65 publications

References 47 publications

Coevolution of variability models and related software artifacts

Coevolution of variability models and related software artifacts

Systematic derivation of correct variability-aware program analyses

A study of variability spaces in open source software

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