Applications of #SAT Solvers on Feature Models

Sundermann, Chico; Nieke, Michael; Bittner, Paul Maximilian; Heß, Tobias; Thüm, Thomas; Schaefer, Ina

doi:10.1145/3442391.3442404

Cited by 21 publications

(23 citation statements)

References 41 publications

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“…Still, there might be other use cases in which an exact number of configurations is required. If that is the case, different product line verification approaches based on #SAT (Sundermann et al 2021) and Binary Decision Diagrams (BDDs) (Cordy et al 2013) should be considered. On a side note, renames-i.e., the Neverlang way of stopping the ripple effect-are not expressed in the FM and cannot be considered when computing valid configurations.…”

Section: Resultsmentioning

confidence: 99%

Towards a recipe for language decomposition: quality assessment of language product lines

Cazzola

Favalli

2022

Empir Software Eng

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Programming languages are complex systems that are usually implemented as monolithic interpreters and compilers. In recent years, researchers and practitioners gained interest in product line engineering to improve the reusability of language assets and the management of variability-rich systems, introducing the notions of language workbenches and language product lines (LPLs). Nonetheless, language development remains a complex activity and design or implementation flaws can easily waste the efforts of decomposing a language specification into language features. Poorly designed language decompositions result in high inter-dependent components, reducing the variability space of the LPL system and its maintainability. One should detect and fix the design flaws posthaste to prevent these risks while minimizing the development overhead. Therefore, various aspects of the quality of a language decomposition should be quantitatively measurable through adequate metrics. The evaluation, analysis and feedback of these measures should be a primary part of the engineering process of a LPL. In this paper, we present an exploratory study trying to capture these aspects by introducing a design methodology for LPLs; we define the properties of a good language decomposition and adapt a set of metrics from the literature to the framework of language workbenches. Moreover, we leverage the LPL engineering environment to perform an empirical evaluation of 26 -based LPLs based on this design methodology. Our contributions form the foundations of a design methodology for -based LPLs. This methodology is comprised of four different elements: i) an engineering process that defines the order in which decisions are made, ii) an integrated development environment for LPL designers and iii) some best practices in the design of well-structured language decomposition when using , supported by iv) a variety of LPL metrics that can be used to detect errors in design decisions.

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Section: Resultsmentioning

confidence: 99%

Towards a recipe for language decomposition: quality assessment of language product lines

Cazzola

Favalli

2022

Empir Software Eng

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“…Its generated scores prioritized features based on their influence on model predictions, guiding feature selection and enhancing the model's interpretability [33]. Combining both approaches allowed for a comprehensive understanding of variable relationships, aiding in effective feature selection and improving the overall performance of machine learning models [34].…”

Section: Feature Importance and Correlation Analysismentioning

confidence: 99%

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

Sarpong,

Akowuah,

Darko

et al. 2024

Preprint

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Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of gari. This domain remains unexplored thus far. A total of 196 graters were studied. Key variables studied included tooth diameter (TD), tooth height (TH), inter-tooth spacing (ITS), drum speed (DS), clearance (C), and moisture content of cassava (MC). Geometric mean diameter (GMD) represented mash quality. Feature importance rankings emphasized TH (0.488784), C (0.243284), TD (0.112682), ITS (0.103547), DS (0.036261), and MC (0.015442) in determining particle size (GMD) of grated mash. Machine learning models efficiently interpreted these attributes, including gradient boost regressor, linear regression, neural network, and random forest. The gradient boost regressor was the best predictive model, achieving 95.34% accuracy, RMSE (0.3291), and MAE (0.2303). The study provides a GMD predictive equation and optimized parameters for specific gari size production, offering valuable insights for tailored machinery in the cassava grating activity.

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“…(iv) and ( 4). (iv) can be checked with a SAT solver [41]. Programs that cannot be transformed into normal form are rejected by the compiler.…”

Section: Syntax Of Abs Variability Modulesmentioning

confidence: 99%

Variability modules for Java-like languages

Damiani

Hähnle

Kamburjan

et al. 2021

Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A

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A Software Product Line (SPL) is a family of similar programs (called variants) generated from a common artifact base. A Multi SPL (MPL) is a set of interdependent SPLs (i.e., such that an SPL's variant can depend on variants from other SPLs). MPLs are challenging to model and implement efficiently, especially when different variants of the same SPL must coexist and interoperate. We address this challenge by introducing variability modules (VMs), a new language construct. A VM represents both a module and an SPL of standard (variabilityfree), possibly interdependent modules. Generating a variant of a VM triggers the generation of all variants required to fulfill its dependencies. Then, a set of interdependent VMs represents an MPL that can be compiled into a set of standard modules. We illustrate VMs by an example from an industrial modeling scenario, formalize them in a core calculus, provide an implementation for the Java-like modeling language ABS, and evaluate VMs by case studies. CCS CONCEPTS• Software and its engineering → Software product lines; Modules / packages; Classes and objects.

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Applications of #SAT Solvers on Feature Models

Cited by 21 publications

References 41 publications

Towards a recipe for language decomposition: quality assessment of language product lines

Towards a recipe for language decomposition: quality assessment of language product lines

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

Variability modules for Java-like languages

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