QFLan: A Tool for the Quantitative Analysis of Highly Reconfigurable Systems
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QFLan offers modeling and analysis of highly reconfigurable systems, like product lines, which are characterized by combinatorially many system variants (or products) that can be obtained via different combinations of installed features. The tool offers a modern integrated development environment for the homonym probabilistic feature-oriented language. QFLan allows the specification of a family of products in terms of a feature model with quantitative attributes, which defines the valid feature combinations, and probabilistic behavior subject to quantitative constraints. The language's behavioral part enables dynamic installation, removal and replacement of features. QFLan has a discrete-time Markov chain semantics, permitting quantitative analyses. Thanks to a seamless integration with the statistical model checker MultiVeStA, it allows for analyses like the likelihood of specific behavior or the expected average value of non-functional aspects related to feature attributes.
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The enhanced CPC learns effective representations by training to distinguish a z-vector k timesteps in the future from a collection of distractor samples drawn from a proposal distribution.
“Similar to the original formulation of CPC [37], the enhanced version of CPC learns effective representations by training to distinguish a z -vector ( z t + k ), that is k timesteps in the future, from a collection of distractor samples drawn from a proposal distribution.”
From Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition (2023) · cites this paper as [37]
The Core layer section describes QFLan’s probabilistic simulator, while the Machine section describes a vending-machine model. Searches for the claim’s wording and for contrastive predictive coding, future latent vectors, and proposal-distribution distractors found no passage addressing the claimed CPC training method. The cited paper therefore does not verify this claim. (LLM, conf=0.78)
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- Authors
- Vandin, A., ter Beek, M. H., Legay, A., & Lluch Lafuente, A.
- Affiliations
- Technical University of Denmark (DK)
- Centre Inria de l'Université de Rennes (FR)
- Corresponding author
- Vandin, Andrea
- Venue
- EasyChair Preprints
- Publisher
- EasyChair
- Published
- 2018-07-20
- Pages
- 329-337
- ISSN
- 0302-9743 (ISSN-L)
- 2516-2314
- OpenAlex
- W2808965408
- Cited by
- 196
- References
- 36
- Citations by year
- 2026: 11 · 2025: 61 · 2024: 68 · 2023: 22 · 2022: 16 · 2021: 9 · 2020: 3 · 2019: 6
- FWCI
- 1.83
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- EasyChair preprint (publishedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion, other-oa)
- ISTI Open Portal (submittedVersion, other-oa)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Physical Sciences › Computer Science › Software › Model-Driven Software Engineering Techniques
- Keywords
- feature-oriented programming, product lines, feature models, quantitative analysis, discrete-time Markov chains, statistical model checking, non-functional properties, reconfigurable systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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