Software Product Lines

Pohl, Klaus; Metzger, Andreas

doi:10.1007/978-3-319-73897-0_11

Cited by 9 publications

(2 citation statements)

References 35 publications

(40 reference statements)

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“…Software Product Line Engineering (SPLE) [19][20][21][22] is an approach to manage and derive different variants of software, the product. There are two phases: domain engineering and application engineering.…”

Section: Software Product Line Engineeringmentioning

confidence: 99%

Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

et al. 2022

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For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or Ansible, support conditional components and dependencies which allow modeling different deployment variants of a composite application in a single deployment model. However, there are deployment technologies, such as TOSCA and Docker Compose, which do not support such conditional elements. To address this, we extend the Essential Deployment Metamodel (EDMM) by conditional components and dependencies. EDMM is a declarative deployment model which can be mapped to several deployment technologies including Terraform, Ansible, TOSCA, and Docker Compose. Preprocessing such an extended model, i.e., conditional elements are evaluated and either preserved or removed, generates an EDMM conform model. As a result, conditional elements can be integrated on top of existing deployment technologies that are unaware of such concepts. We evaluate this by implementing a preprocessor for TOSCA, called OpenTOSCA Vintner, which employs the open-source TOSCA orchestrators xOpera and Unfurl to execute the generated TOSCA conform models.

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Section: Software Product Line Engineeringmentioning

confidence: 99%

Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

et al. 2022

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“…The main advantages of systematic reuse in SPLs are the reduction of development costs and the time-to-market, and an increase in software quality (McGovern et al 2003;Pohl et al 2005). However, adopting an SPL strategy requires expensive up-front investment (Martinez 2016) for defining the SPL scope and product portfolio offered by a software company (Pohl and Metzger 2018). Further, it is necessary to define which set of features and which set of domain artifacts can be reused for product composition.…”

Section: Introductionmentioning

confidence: 99%

Evolving software system families in space and time with feature revisions

et al. 2022

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Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.

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