TypeChef

Kenner, Andy; Kästner, Christian; Haase, S.; Leich, Thomas

doi:10.1145/1868688.1868693

Cited by 56 publications

(1 citation statement)

References 25 publications

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“…TypeChef is a type-checker for product lines written in C where variability is realized using preprocessor directives [14]. It implements a partial preprocessor that tries to extract information from variability code.…”

Section: Analysis Of Typechefmentioning

confidence: 99%

A Comparative Study on Variability Code Analysis Technology

Nair

Becker

Tenev

2020

Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B

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Product line engineering is often conducted in an incremental way, in which the variability artifacts evolve in the space, the time, as well as the asset dimension. In order to cope with the evolution of the variability, the VITAL approach and tool have been developed and used in different industrial settings to analyze variability realizations relying on the C preprocessor. Over the last decade, further promising analysis approaches and tools have been developed. To understand, if and how they could enhance the VITAL approach, we have conducted an analysis of promising technologies. In this paper, we share some of our findings along our comparative study on variability code analysis technologies. As we have conducted the study in the light of the intended VITAL enhancement, the study does not claim completeness. Nevertheless, we believe that the findings can help researchers and industrial practitioners to gain an overview and find entry points for their own investigations.

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Section: Analysis Of Typechefmentioning

confidence: 99%

A Comparative Study on Variability Code Analysis Technology

Nair

Becker

Tenev

2020

Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B

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Composing annotations without regret? Practical experiences using FeatureC

Krüger

Pinnecke

Kenner³

et al. 2017

Softw Pract Exp

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Software product lines enable developers to derive similar products from a common code base. Existing implementation techniques can be categorized as composition-based and annotation-based approaches, with both approaches promising complementary benefits. However, annotation-based approaches are commonly used in practice despite composition allowing physical separation of features and, thus, improving traceability and maintenance. A main hindrance to migrate annotated systems toward a composition-based product line is the challenging and time-consuming transformation task. For a company, it is difficult to predict the corresponding costs, and a successful outcome is uncertain. To overcome such problems, a solution proposed by the previous work is to use a hybrid approach, utilizing composition and annotation simultaneously. Based on this idea, we introduce a stepwise migration process from annotation-based toward composition-based approaches to lower the adoption barrier of composition. This process itself is independent of used implementation techniques and enables developers to incrementally migrate toward composition. We support our approach with detailed examples by partially migrating a real-world system.In detail, we describe the following: (1) our migration process, (2) its application on a real-world system, and (3) discuss practical challenges we face. We implemented the proposed approach and show that appropriate tool support helps to migrate toward composition-based product lines. Based on the case study, we show that the hybrid product lines work correctly and can compete with the performance of the original annotated system. However, the results also illustrate open issues that have to be solved to apply such migrations in practice.

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Scaling up the Fitness Function for Reverse Engineering Feature Models

Thianniwet

Cohen

2016

Search Based Software Engineering

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TypeChef

Cited by 56 publications

References 25 publications

A Comparative Study on Variability Code Analysis Technology

A Comparative Study on Variability Code Analysis Technology

Composing annotations without regret? Practical experiences using FeatureC

Scaling up the Fitness Function for Reverse Engineering Feature Models

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