Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features

Munoz, Daniel-Jesús; Oh, Jeho; Pinto, Mónica; Fuentes, Lidia; Batory, Don

doi:10.1145/3336294.3336297

Cited by 43 publications

(38 citation statements)

References 50 publications

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“…These options cannot be arbitrarily combined, as every option typically comes with constraints with respect to several other options. Constraints are specified in KConfig [21], but can be translated into feature models or propositional logic [11,32,34,37,40,49,57,65,68,71,77,[81][82][83]. Whenever we analyze the Linux kernel for errors, ignoring those constraints would lead to false positives.…”

Section: Motivationmentioning

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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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: Motivationmentioning

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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“…Examples of language constructs are Feature to represent the concept of a feature, Root to represent the root feature of the feature model, OptionalFeature and MandatoryFeature to represent optional and mandatory features, respectively, AlternativeGroup for "xor" and OrGroup for "or" feature groups, Multi-Feature for clonable features [25], NumericalFeature for non-Boolean numerical features [43], or FeatureAttribute for attributed features [14]. Here, we also define a special language construct for feature models: Definition 3.6 (Feature model construct).…”

Section: Formalization Of Our Approachmentioning

confidence: 99%

“…That is, is the main containment element in the metamodel (aka, the root element). 1 Examples of feature model constructs are Feature Model to represent the most generic feature model, Cardinality-Based FM to represent feature models with cardinalities [25], Attributed FM for models that support features with attributes [14], Numerical FM for models with numerical features [43], etc.…”

Section: Formalization Of Our Approachmentioning

confidence: 99%

“…Since the introduction of feature models 30 years ago [38], numerous language constructs have been proposed to increase their expressiveness [13], for example, cardinality-based variability [25,42], multi-features [25]; numerical features [43] or non-Boolean features; attributed feature models [14]; and complex constraints [4]. Each of those language constructs usually comes with its syntax and semantics, and often coincides with the definition of a completely new modeling language or a new tool to support it [60].…”

Section: Introductionmentioning

confidence: 99%

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Extensible and modular abstract syntax for feature modeling based on language constructs

Horcas¹,

Pinto²,

Fuentes³

2020

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

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Since the definition of feature models in 1990, a large number of language constructs have emerged. Each language construct usually comes with its own abstract and concrete syntax, its semantics, and even its complete language dialect and tool support. Nowadays, there is a consensus in the Software Product Line community about a need for defining a common variability modeling language. But the fact of the matter is that it is very complex to achieve a good compromise between how expressive the language should be and the effort of developing practical tools for a language with all possible language constructs. In this paper, we propose an extensible model-driven engineering approach for defining the abstract syntax of feature modeling language constructs that could be tailored to different needs and domains. We formalize our approach as a set of modular and reusable metamodels that allows practitioners to decide which subset of language constructs to use through: (1) generating a new variability language; and (2) managing feature models with different level of expressiveness. We provide an instantiation and implementation of our approach.

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“…However, Boolean features are insufficient for real-world program families, as there exist features that have a range of numbers as possible values. These features are called numerical features [25]. For instance, Linux kernel, BusyBox, Apache web server, Java Garbage Collector represent some real-world program families with numerical features.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features

Dimovski¹,

Apel

Legay

2021

Fundamental Approaches to Software Engineering

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Lifted (family-based) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted analysis domain are tuples, which maintain one property per variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces.The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and $$\texttt {\#if}$$ # if directives). In this work, we introduce a new symbolic representation of the lifted abstract domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. The elements of the new lifted domain are constraint-based decision trees, where decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. To illustrate the potential of this representation, we have implemented an experimental lifted static analyzer, called SPLNum$$^2$$ 2 Analyzer, for inferring invariants of C programs. An empirical evaluation on BusyBox and on benchmarks from SV-COMP yields promising preliminary results indicating that our decision trees-based approach is effective and outperforms the baseline tuple-based approach.

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Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features

Cited by 43 publications

References 50 publications

A BDD for Linux?

A BDD for Linux?

Extensible and modular abstract syntax for feature modeling based on language constructs

A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features

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