Higher-order delta modeling for software product line evolution

Lity, Sascha; Kowal, Matthias; Schaefer, Ina

doi:10.1145/3001867.3001872

Cited by 14 publications

(16 citation statements)

References 29 publications

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“…3. In previous work [24], we introduced higher-order delta modeling as an extension of delta modeling [9] to also capture SPL evolution, where we focused on the differences between SPL versions. To exploit the modeling benefits of both formalisms, we present a bidirectional transformation in Sect.…”

Section: Foundationsmentioning

confidence: 99%

“…Higher-Order Delta Modeling. For the evolution of an SPL defined by a DM SPL , we introduced an extension of delta modeling [9] in previous work [24]. Higher-order delta modeling specifies the evolution of a DM SPL into its new version DM ′ SPL via higher-order deltas.…”

Section: Foundationsmentioning

confidence: 99%

“…The application of a higher-order Fig. 2, where we adapted higher-order delta modeling [24] for state machines. Based on the core state machine shown in Fig.…”

Section: Foundationsmentioning

confidence: 99%

“…Existing approaches consider SPL evolution on the feature model level [31,32,37], by adopting delta modeling [15,24,38], by using aspect-oriented programming [2,3,13], or specify SPL evolution templates [29]. However, to the best of our knowledge no approach exists in the literature for the annotative category of variability modeling techniques such that version as well as variant information is captured together in a single model, i.e., elements are annotated with both version and variability conditions.…”

mentioning

confidence: 99%

“…(2) We discuss benefits and limitations of 175% modeling and propose potential application scenarios. (3) 175% modeling does not support, e.g., incremental changeoriented analysis of evolution steps which is crucial for SPL regression testing as, e.g., provided by higher-order delta modeling [24]. To overcome this drawback and to exploit the benefits of both formalisms, we provide a bidirectional transformation between 175% and higher-order delta models, where one type is used to derive the opposing type.…”

mentioning

confidence: 99%

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175% Modeling for Product-Line Evolution of Domain Artifacts

Lity

Nahrendorf

Thüm

et al. 2018

Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

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Software evolution is an inevitable process in the development of long-living software systems as, e.g., changes of requirements demand corresponding adaptations. For software product lines, the incorporation of evolution in the development process gets even more complex due to the vast number of potential variants and the set of reusable domain artifacts and their interrelations. To allow for the application of existing analyses also for combined dimensions of variants and versions, recent evolution-aware variability modeling techniques are insufficient for capturing both version and variant information by the same means. In this paper, we propose an extension of annotative variability modeling, also known as 150% modeling, to tackle evolution and variability by the same means. The so called 175% modeling formalism allows for the development and documentation of evolving product lines. A 175% model combines all variant-specific models of all versions of a product line, where elements are mapped to features and versions to specify which version of a variant contains the element. We discuss potential application scenarios for 175% modeling. Furthermore, we propose a bidirectional transformation between 175% and higher-order delta models to exploit the benefits of both modeling formalisms, when solely one type is available. CCS CONCEPTS • Software and its engineering → Software product lines; Model-driven software engineering; Software evolution;

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Section: Foundationsmentioning

confidence: 99%

Section: Foundationsmentioning

confidence: 99%

“…The application of a higher-order Fig. 2, where we adapted higher-order delta modeling [24] for state machines. Based on the core state machine shown in Fig.…”

Section: Foundationsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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175% Modeling for Product-Line Evolution of Domain Artifacts

Lity

Nahrendorf

Thüm

et al. 2018

Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

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Taming Multi-Variability of Software Product Line Transformations

Strüber

Peldzsus

Jürjens

2018

Fundamental Approaches to Software Engineering

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Software product lines continuously undergo model transformations, such as refactorings, refinements, and translations. In product line transformations, the dedicated management of variability can help to control complexity and to benefit maintenance and performance. However, since no existing approach is geared for situations in which both the product line and the transformation specification are affected by variability, substantial maintenance and performance obstacles remain. In this paper, we introduce a methodology that addresses such multivariability situations. We propose to manage variability in product lines and rule-based transformations consistently by using annotative variability mechanisms. We present a staged rule application technique for applying a variability-intensive transformation to a product line. This technique enables considerable performance benefits, as it avoids enumerating products or rules upfront. We prove the correctness of our technique and show its ability to improve performance in a software engineering scenario.

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Incremental software product line verification - A performance analysis with dead variable code

et al. 2022

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Verification approaches for Software Product Lines (SPL) aim at detecting variability-related defects and inconsistencies. In general, these analyses take a significant amount of time to provide complete results for an entire, complex SPL. If the SPL evolves, these results potentially become invalid, which requires a time-consuming re-verification of the entire SPL for each increment. However, in previous work we showed that variability-related changes occur rather infrequently and typically only affect small parts of a SPL. In this paper, we utilize this observation and present an incremental dead variable code analysis as an example for incremental SPL verification, which achieves significant performance improvements. It explicitly considers changes and partially updates its previous results by re-verifying changed artifacts only. We apply this approach to the Linux kernel demonstrating that our fastest incremental strategy takes only 3.20 seconds or less for most of the changes, while the non-incremental approach takes 1,020 seconds in median. We also discuss the impact of different variants of our strategy on the overall performance, providing insights into optimizations that are worthwhile.

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Higher-order delta modeling for software product line evolution

Cited by 14 publications

References 29 publications

175% Modeling for Product-Line Evolution of Domain Artifacts

175% Modeling for Product-Line Evolution of Domain Artifacts

Taming Multi-Variability of Software Product Line Transformations

Incremental software product line verification - A performance analysis with dead variable code

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