Implicit constraints in partial feature models

Ananieva, Sofia; Kowal, Matthias; Thüm, Thomas; Schaefer, Ina

doi:10.1145/3001867.3001870

Cited by 41 publications

(9 citation statements)

References 27 publications

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“…These include automated analyses of feature models [9,38] and analyses also incorporating other domain artifacts [86,92]. In particular, a logical representation has been used for feature-model evolution [67,87], feature-model interfaces and slicing [1,74], computation of implicit constraints [3], product configuration [44,72] including staged configuration [28], parsing [50], dead-code analysis [83], code simplification [93], type checking [85], consistency checking [29], dataflow analyses [56], model checking [19], testing [16] including variability-aware execution [66] and sampling [58,89], optimization of non-functional properties [78], and variant-preserving refactoring [35]. Each of these analyses may profit from knowledge compilation.…”

Section: State-of-the-artmentioning

confidence: 99%

“…2 These models have been translated with an extension of the Linux Variability Analysis Tools (LVAT) [11]. 3 The advantage over the previous benchmark is that the hierarchy of features is available and that models are available in a version without newly introduced variables. While the benchmark also contains Linux, the feature model represents version 2.6.33.3, which has been released in April 2010 and used for illustration in Figure 1.…”

Section: Call For Contributionsmentioning

confidence: 99%

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A BDD for Linux?

Thüm

2020

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

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What is the number of valid configurations for Linux? How to generate uniform random samples for Linux? Can we create a binary decision diagram for Linux? It seems that the product-line community tries hard to answer such questions for Linux and other configurable systems. However, attempts are often not published due to the publication bias (i.e., unsuccessful attempts are not published). As a consequence, researchers keep trying by potentially spending redundant effort. The goal of this challenge is to guide research on these computationally complex problems and to foster the exchange between researchers and practitioners.

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Section: State-of-the-artmentioning

confidence: 99%

Section: Call For Contributionsmentioning

confidence: 99%

A BDD for Linux?

Thüm

2020

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

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“…Satisfiability solving is a ubiquitous technology in software product lines for a diverse set of analyses ranging from anomaly detection [2,38,46], dead code analysis [62], sampling [47,65], and automated analysis of feature models [10,32,64]. The general pattern is to represent parts of the system or feature model as a propositional formula [9,25,48], and reduce the analysis to a satisfiability (SAT) problem.…”

Section: Introductionmentioning

confidence: 99%

“…FM 0 contains no spectre/meltdown-related features; FM 1 contains a set of new features named spectre_v2, nospec_store_bypass_disable, l1tf, and pti; and FM 2 contains a single feature mitigations that combines all of the exploit prevention features from FM 1 . 2 We introduce some notation to track particular features and propositional formulas across multiple feature models. For features we use f i.j to refer to the th feature in the th feature model.…”

Section: Introductionmentioning

confidence: 99%

Variational satisfiability solving

Young

Walkingshaw

Thüm

2020

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

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Incremental satisfiability (SAT) solving is an extension of classic SAT solving that allows users to efficiently solve a set of related SAT problems by identifying and exploiting shared terms. However, using incremental solvers effectively is hard since performance is sensitive to a problem's structure and the order sub-terms are fed to the solver, and the burden to track results is placed on the end user. For analyses that generate sets of related SAT problems, such as those in software product lines, incremental SAT solvers are either not used at all, used but not explicitly stated so in the literature, or used but suffer from the aforementioned usability problems. This paper translates the ordering problem to an encoding problem and automates the use of incremental SAT solving. We introduce variational SAT solving, which differs from incremental SAT solving by accepting all related problems as a single variational input and returning all results as a single variational output. Our central idea is to make explicit the operations of incremental SAT solving, thereby encoding differences between related SAT problems as local points of variation. Our approach automates the interaction with the incremental solver and enables methods to automatically optimize sharing of the input. To evaluate our methods we construct a prototype variational SAT solver and perform an empirical analysis on two real-world datasets that applied incremental solvers to software evolution scenarios. We show, assuming a variational input, that the prototype solver scales better for these problems than naive incremental solving while also removing the need to track individual results. CCS CONCEPTS • Software and its engineering → Software product lines; • Hardware → Theorem proving and SAT solving.

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“…A CPPS can interact with its environment, make context-aware decisions, and self-adapt to uncertain conditions [19,32], supporting the envisioned flexibility. Recent research shows growing interest in applying SPL engineering concepts to CPPS engineering [1,7,24,30].…”

Section: Introductionmentioning

confidence: 99%

Integrating Variability Modeling of Products, Processes, and Resources in Cyber-Physical Production Systems Engineering

Meixner

2020

Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B

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The Industry 4.0 initiative envisions the flexible and optimized production of customized products on Cyber-Physical Production Systems (CPPSs) that consist of subsystems coordinated to conduct complex production processes. Hence, accurate CPPS modeling requires integrating the modeling of variability for Product-Process-Resource (PPR) aspects. Yet, current variability modeling approaches treat structural and behavioral variability separately, leading to inaccurate CPPS production models that impede CPPS engineering and optimization. This paper proposes a PhD project for integrated variability modeling of PPR aspects to improve the accuracy of production models with variability for CPPS engineers and production optimizers. The research project follows the Design Science approach aiming for the iterative design and evaluation of (a) a framework to categorize currently incomplete and scattered models and methods for PPR variability modeling as a foundation for an integrated model; and (b) a modeling approach for more accurate integrated PPR variability modeling. The planned research will provide the Software Product Line (SPL) and CPPS engineering research communities with (a) novel models, methods, and insights on integrated PPR variability modeling, (b) open data from CPPS engineering use cases for common modeling, and (c) empirical data from field studies for shared analysis and evaluation. CCS CONCEPTS • Software and its engineering → Software product lines.

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Implicit constraints in partial feature models

Cited by 41 publications

References 27 publications

A BDD for Linux?

A BDD for Linux?

Variational satisfiability solving

Integrating Variability Modeling of Products, Processes, and Resources in Cyber-Physical Production Systems Engineering

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