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

Schlie, Alexander; Knüppel, Alexander; Seidl, Christoph; Schaefer, Ina

doi:10.1145/3382025.3414973

Cited by 8 publications

(3 citation statements)

References 70 publications

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“…This is also an important topic in software product line engineering. Many techniques have been proposed to locate features, synthesize FMs out of artifacts, or recover an architecture out of variants [1,20,22,2,31]. In our study, we have started with a manual approach when recovering the informal documentation.…”

Section: Related Workmentioning

confidence: 99%

Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

Tërnava

Acher

Lesoil

et al. 2022

Reuse and Software Quality

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Numerous software systems are configurable through compiletime options and the widely used ./configure. However, the combined effects of these options on binary's non-functional properties (size and attack surface) are often not documented, and or not well understood, even by experts. Our goal is to provide automated support for exploring and comprehending the configuration space (a.k.a., surface) of compile-time options using statistical learning techniques. In this paper, we perform an empirical study on four C-based configurable systems. We measure the variation of binary size and attack surface (by quantifying the number of code reuse gadgets) in over 400 compile-time configurations of a subject system. We then apply statistical learning techniques on top of our build infrastructure to identify how compile-time options relate to non-functional properties. Our results show that, by changing the default configuration, the system's binary size and gadgets vary greatly (roughly −79% to 244% and −77% to 30%, respectively). Then, we found out that identifying the most influential options can be accurately learned with a small training set, while their relative importance varies across size and attack surface for the same system. Practitioners can use our approach and artifacts to explore the effects of compile-time options in order to take informed decisions when configuring a system with ./configure.

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Section: Related Workmentioning

confidence: 99%

Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

Tërnava

Acher

Lesoil

et al. 2022

Reuse and Software Quality

View full text Add to dashboard Cite

show abstract

“…We also included five Simulink subjects. Three of them are case studies taken from Schlie et al [58,59]: DAS, a driver assistance system from the SPES_XT project [60], and APS, [62], we mined GitHub for open-source projects with Simulink models. We found 317 distinct projects with 4,402 Simulink models.…”

Section: Simulink Subjectsmentioning

confidence: 99%

RaQuN: a generic and scalable n-way model matching algorithm

et al. 2022

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Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on $$\text {N}$$ N input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.

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“…Schlie et.al. [44] proposed incremental feature model synthesis for clone-and-own software systems in models. Their synthesis only considers feature model elements without cross-tree constraints.…”

Section: Synthesized Multi Layer Feature-modelmentioning

confidence: 99%

Extractive Multi Product-Line Engineering

Rosiak

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Cloning is a general approach to create new functionality within variants as well as new system variants. It is a fast, flexible, intuitive, and economical approach to evolve systems in the short run. However, in the long run, the maintenance effort increases. A common solution to this problem is the extraction of a product line from a set of cloned variants. This process requires a detailed analysis of variants to extract variability information. However, clones within a variant are usually not considered in the process, but are also a cause for unsustainable software. This thesis proposes an extractive multi product-line engineering approach to re-establish the sustainable development of software variants. We propose an approach to re-engineer intra-system and inter-system clones into reusable, configurable components stored in an integrated platform and synthesize a matching multilayer feature model.

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

Cited by 8 publications

References 70 publications

Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

RaQuN: a generic and scalable n-way model matching algorithm

Extractive Multi Product-Line Engineering

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