Guaranteeing Syntactic Correctness for All Product Line Variants: A Language-Independent Approach

Kästner, Christian; Apel, Sven; Trujillo, Salvador; Kuhlemann, Martin; Batory, Don

doi:10.1007/978-3-642-02571-6_11

Cited by 53 publications

(44 citation statements)

References 30 publications

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“…Whereas many concern-location tools (such as Suade [52] and Cerberus [22]) use rather lightweight models and cover only entire methods and fields, we need fine granularity at intraprocedural level, as argued above. For Java, we model compilation units, types, fields, methods, statements, local variables, parameters, and import declarations (we discussed suitable granularity for product lines in prior work [29], [40]; in languages other than Java, we could chose similar structures [32]). Technically, we automatically extract elements from abstract syntax trees provided by Eclipse.…”

Section: Underlying Modelmentioning

confidence: 99%

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Kästner

Dreiling

Ostermann

2014

IIEEE Trans. Software Eng.

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Abstract-Software product line engineering is an efficient means to generate a set of tailored software products from a common implementation. However, adopting a product-line approach poses a major challenge and significant risks, since typically legacy code must be migrated toward a product line. Our aim is to lower the adoption barrier by providing semiautomatic tool support-called variability mining-to support developers in locating, documenting, and extracting implementations of product-line features from legacy code. Variability mining combines prior work on concern location, reverse engineering, and variability-aware type systems, but is tailored specifically for the use in product lines. Our work pursues three technical goals: (1) we provide a consistency indicator based on a variability-aware type system, (2) we mine features at a fine level of granularity, and (3) we exploit domain knowledge about the relationship between features when available. With a quantitative study, we demonstrate that variability mining can efficiently support developers in locating features.

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Section: Underlying Modelmentioning

confidence: 99%

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Kästner

Dreiling

Ostermann

2014

IIEEE Trans. Software Eng.

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“…A different line of research aims at the language-independent reasoning about features [15,48,10,43]. The calculus gDeep is most closely related to FFJ since it provides a type system for feature-oriented languages that is language-independent [5,4].…”

Section: Feature-oriented Programsmentioning

confidence: 99%

Type safety for feature-oriented product lines

et al. 2010

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A feature-oriented product line is a family of programs that share a common set of features. A feature implements a stakeholder's requirement and represents a design decision or configuration option. When added to a program, a feature involves the introduction of new structures, such as classes and methods, and the refinement of existing ones, such as extending methods. A feature-oriented decomposition enables a generator to create an executable program by composing feature code solely on the basis of the feature selection of a user -no other information needed. A key challenge of product line engineering is to guarantee that only well-typed programs are generated. As the number of valid feature combinations grows combinatorially with the number of features, it is not feasible to type check all programs individually. The only feasible approach is to have a type system check the entire code base of the feature-oriented product line. We have developed such a type system on the basis of a formal model of a feature-oriented Java-like language. The type system guaranties type safety for feature-oriented product lines. That is, it ensures that every valid program of a well-typed product line is well-typed. Our formal model including type system is sound and complete.

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“…Language independent frameworks [16,125] operate at the product line level for reference checking-checking which dependencies are present and satisfied-and for checking syntactic correctness.…”

Section: Type Systems and Static Analysismentioning

confidence: 99%

Software diversity: state of the art and perspectives

Schaefer

Rabiser²,

Clarke

et al. 2012

Int J Softw Tools Technol Transfer

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136

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Abstract. Diversity is prevalent in modern software systems to facilitate adapting the software to customer requirements or the execution environment. Diversity has an impact on all phases of the software development process. Appropriate means and organizational structures are required to deal with the additional complexity introduced by software variability. This introductory article to the special section "Software Diversity -Modeling, Analysis and Evolution" provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions. The article covers requirements analysis, design, implementation, verification and validation, maintenance and evolution as well as organizational aspects. It also provides an overview of the articles which are part of this special section and address particular issues of diverse systems development.

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Guaranteeing Syntactic Correctness for All Product Line Variants: A Language-Independent Approach

Cited by 53 publications

References 30 publications

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Type safety for feature-oriented product lines

Software diversity: state of the art and perspectives

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