Reengineering Variants of MATLAB/Simulink Software Systems

Schlie, Alexander; Seidl, Christoph; Schaefer, Ina

doi:10.1007/978-3-030-25312-7_10

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…SCMA [Schlie et al 2018], [Schlie et al 2019] is a procedure that enables the evaluation of multiple MATLAB/Simulink model variants simultaneously. The transformation of models into a matrix form reduces the complexity of the models and allows large-scale systems to be compared with each other in their entirety.…”

Section: Methodsmentioning

confidence: 99%

“…ACC in Figure 5-13(b)). Subsequently, the workflow shown in Figure 5-13(a) can be applied in a fine-grained fashion to only those components warranting such analysis, for instance to locate variation points at a fine level of detail [Schlie et al 2017] and to derive a final 150% model [Schlie et al 2019]. Such a 150% model (cf.…”

Section: Family Mining -A Methods For Extracting Reference Architecturmentioning

confidence: 99%

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Architectures for Dynamically Coupled Systems

Gandor

Jäckel

Käser³

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

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Dynamically coupled collaborative embedded systems operate in groups that form, change, and dissolve—often frequently—during their lifetime. Furthermore, the context in which collaborative systems operate is a dynamic one: systems in the context may appear, change their visible behavior, and disappear again. Ensuring safe operation of such collaborative systems is of key importance, while their dynamic nature poses challenges that do not occur in “classical” system design. This starts with the elicitation of the operational context against which the system will be designed—requiring capture of its dynamic nature—and affects all other design phases as well. Novel development methods are required, enabling engineers to deal with the challenges raised by dynamicity in a manageable way. This chapter presents methods that have been developed to support engineers in this task. The methods cover different viewpoints and abstraction levels of the development process, starting at the requirements viewpoint, and glance at the functional and technical design, as well as verification methods for the type of systems envisioned.

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Section: Methodsmentioning

confidence: 99%

Section: Family Mining -A Methods For Extracting Reference Architecturmentioning

confidence: 99%

Architectures for Dynamically Coupled Systems

Gandor

Jäckel

Käser³

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

Self Cite

View full text Add to dashboard Cite

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“…Prior work on re-engineering variability in our group [55,76,[78][79][80][81] focuses on the solution space for MATLAB/Simulink models [64][65][66][67]. Prior work encompasses a coarse-grain variability analysis of an entire model portfolio [64], followed by a fine-grained analysis of individual systems [66,67] and, finally, the combination of both analyses to produce a holistic 150% model that represents solution-space variability for multiple MATLAB/Simulink models [65]. Compiled details on previous work can be found online [11].…”

Section: Prior Work On Solution Space Variabilitymentioning

confidence: 99%

“…Construct Validity: To evaluate our approach, we used specific orders, in which we included variants in the analysis. While we acknowledge that different orders may impact the results, our prior work mitigates this threat by constructing the 150% model, used as basis for our approach independently of input order [64,65]. External Validity: For our evaluation, we used synthetic variants from one domain only.…”

Section: Threats To Validitymentioning

confidence: 99%

Incremental feature model synthesis for clone-and-own software systems in MATLAB/Simulink

Schlie

Knüppel

Seidl

et al. 2020

Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume a - Volume A

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Families of related MATLAB/Simulink systems commonly emerge ad hoc using clone-and-own practices. Extractively migrating systems towards a software product line (SPL) can be a remedy. A feature model (FM) represents all potential configurations of an SPL, ideally, in non-technical domain terms. However, yielding a sensible FM from automated synthesis remains a major challenge due to domain knowledge being a prerequisite for features to be adequate abstractions. In incremental reverse engineering, subsequent generation of FMs may further overwrite changes and design decisions made during previous manual FM refinement.In this paper, we propose an approach to largely automate the synthesis of a suitable FM from a set of cloned MATLAB/Simulink models as part of reverse engineering an SPL. We fully automate the extraction of an initial, i.e., a technical, FM that closely aligns with realization artifacts and their variability, and further provide operations to manually refine it to incorporate domain knowledge. Most importantly, we provide concepts to capture such operations and to replay them on a structurally different technical FM stemming from a subsequent reverse engineering increment that included further systems of the portfolio. We further provide an implementation and demonstrate the feasibility of our approach using two MATLAB/Simulink data sets from the automotive domain. CCS CONCEPTS• Software and its engineering → Software product lines; Software reverse engineering; Maintaining software.

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Reengineering Variants of MATLAB/Simulink Software Systems

Cited by 2 publications

References 28 publications

Architectures for Dynamically Coupled Systems

Architectures for Dynamically Coupled Systems

Incremental feature model synthesis for clone-and-own software systems in MATLAB/Simulink

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