Simulating evolution in model-based product line engineering

Heider, Wolfgang; Froschauer, Roman; Grünbacher, Paul; Rabiser, Rick; Dhungana, Deepak

doi:10.1016/j.infsof.2010.03.007

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…We chose to use this approach since lack of empirical data in product line engineering is a well-known problem (Heider et al, 2010). Apart from that, the size of the feature models was more important for us than the detailed model semantics as we do not use it to derive general conclusions about feature model evolution from the model (like Lotufo et al, 2010) -we rather show that our modeling approach can basically deal successfully with very large feature models.…”

Section: Construct Validitymentioning

confidence: 99%

Model-driven support for product line evolution on feature level

Pleuss

Botterweck

Dhungana

et al. 2012

Journal of Systems and Software

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Please cite this article in press as: Pleuss, A., et al., Model-driven support for product line evolution on feature level. J. Syst. Software (2011), doi:10.1016/j.jss.2011.08.008 Software Product Lines (SPL) are an engineering technique to efficiently derive a set of similar products from a set of shared assets. In particular in conjunction with model-driven engineering, SPL engineering promises high productivity benefits. There is however, a lack of support for systematic management of SPL evolution, which is an important success factor as a product line often represents a long term investment. In this article, we present a model-driven approach for managing SPL evolution on feature level. ARTICLE IN PRESSTo reduce complexity we use model fragments to cluster related elements. The relationships between these fragments are specified using feature model concepts itself leading to a specific kind of feature model called EvoFM. A configuration of EvoFM represents an evolution step and can be transformed to a concrete instance of the product line (i.e., a feature model for the corresponding point in time). Similarly, automatic transformations allow the derivation of an EvoFM from a given set of feature models. This enables retrospective analysis of historic evolution and serves as a starting point for introduction of EvoFM, e.g., to plan future evolution steps.

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Section: Construct Validitymentioning

confidence: 99%

Model-driven support for product line evolution on feature level

Pleuss

Botterweck

Dhungana

et al. 2012

Journal of Systems and Software

Self Cite

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“…Table 1) to simulate realistic product line evolution (Heider et al, 2010). For example, we added decisions and assets about more advanced mobile phone features such as touch screens.…”

Section: Application Example: Evolving the Mobile Phone Product Linementioning

confidence: 99%

Software and Systems Traceability

Cleland‐Huang¹,

Gotel²,

Zisman³

2012

130

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“…Moreover, we simulated the evolution by means of a simulation approach for exploring the effects of product line evolution [28]. The simulator tool allows generating random models based on product line profiles.…”

Section: Validation Of the Dspl Knowledge Evolutionmentioning

confidence: 99%

Achieving Knowledge Evolution in Dynamic Software Product Lines

Arcega

Font

Haugen

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Dynamic Software Product Lines (DSPLs) offer a strategy to deal with software changes that need to be handled at run-time. In response to context changes, a DSPL capitalize on knowledge about the architecture variability of the software system to shift between configurations. Similar to any other kind of software, a DSPL needs to evolve over time but current approaches require software engineers to manually perform the DSPL evolution. Our work addresses the evolution of the architecture variability that makes up the knowledge of the DSPL. Given a new version of the architecture variability, we calculate its configuration space and propose strategies that allow migration from the current version to the new version. Our strategy solves the collision of the realization layer resulting from the integration of the new version of the variability specification. We evaluate our dynamic evolution strategy using the Goal-Question-Metric method for a Smart Hotel case study with 2 39 possible configurations as starting point. Our experiment indicates that the proposed technique would enable automatic evolution in 9 out of 10 cases. In the rest of the cases, all of the DSPL configurations changed between the old and the new version, which frustrates an automatic evolution.

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Simulating evolution in model-based product line engineering

Cited by 16 publications

References 36 publications

Model-driven support for product line evolution on feature level

Model-driven support for product line evolution on feature level

Software and Systems Traceability

Achieving Knowledge Evolution in Dynamic Software Product Lines

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