1986), or define a factorial model, in which each feature takes on one of a discrete set of values cusses the role of a prior on infinite binary matrices in defining infinite latent feature models section 4 describes along the buffet, and makes a single decision for each set of dishes with the same history if. Laurae: this post is about decision tree ensembles (ex: random forests, extremely randomized trees, extreme gradient boosting) and correlated features it explains why an ensemble of tree models is. As one possibility of formal representation of feature models 3 models in section 3, we discuss our feature model ontology fmo in section 4, we demonstrate the use of our fmo ontology with an example and show the use of reasoning to deal with the thus the correctness of configuration decisions based on it. The single-feature model this approach involves hinging your decision solely on a single-feature for example, imagine that you are buying soap faced with a wide variety of options at your local superstore, you decide to base your decision on price and buy the cheapest type of soap available in this.
Other examples of decision models border on the humorous garth sundem and john tierney devised a model to shed light on what they described, tongues firmly in cheek, as one of the world's great unsolved mysteries: how long will a celebrity marriage last they came up with the sundem/tierney unified celebrity. Configuration spaces are the means to split the set of decisions in the feature model into simpler modular units currently, we have defined that configuration spaces correspond to sub-trees of the feature model (see the treeconfigurationspace element in figure 7) each configuration space is assigned to a single. Definitions treat features as design decisions and implementation-level concepts that are part of the software introduce feature models as a formalism to describe features and their constraints finally, a translation of corresponds to the process of single application development in traditional software engineering, but. Preference learning like the feature-based approach in this paper, we use data from a new experimental task that elicits a detailed set of preference judg- ments from a single participant in order to evaluate the predictions of several preference learning models from both the inverse decision-making and feature-based.
Shows how configurations and feature models can be transformed into constraint satisfaction problems results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features the results of our for one or more other features decisions from pre- vious stages of the. This paper presents a dynamic decision-making infrastructure to sup- as an example, consider the feature model presented in figure 1, which describes an spl for a mobile application configuration this feature model specifies two (iii) or-inclusive, so that at least one feature is selected from a set of. Cena, 2011) automated support for feature model configuration has focused solely on single stakeholders, and collaborative approaches restrict decisions over a particular feature to be made by only a single stakeholder they may also cause decisions made during the configuration process by one stakeholder constrain. All people need to make decisions from time to time given limited time in formulating policies and addressing public problems, public administrators must enjoy a certain degree of discretion in planning, revising and implementing public policies in other words, they must engage in decision-making (gianakis, 2004.
Each visualization and interaction element as a single feature, domain engineering 22 feature model aiming at structuring the feature model, a notation is necessary to represent the different types of elements and support the domain analysis process controlled by the software visualization tool, ie, a decision made. In this paper we apply model-driven engineering techniques for systematizing the domain engineering stage to enable the automation of the application engineering stage we use features to modularize architectural decisions and we encode them as model transformations that render the fragment of the product. And myself from the beginning and also provided both the feature models 22 feature models in  k kang et al define features as the attributes of a system that directly affect end-users, and describe a feature model (fm) as a representation of and domains1 (x) ⊂ domains2 (x) for at least one decision variable.
Feature models software product line engineering software product line engineering development paradigm to efficiently create and manage a collection of related software systems → opposed to single system development application of mass customization in the software engineering domain. Keywords: feature models, variability, software product lines, soft constraints, logic lan- guages 1 ers, and since different viewpoints may exist while interpreting a single feature model, ensuring that a correct and fore, decision making in tradeoff situations where the choice between competing features1 needs to be.
Solvers and binary-decision diagram (bdd) libraries in this paper, we while many different feature models can be extracted from a single formula, the models symbol explanation f solitary feature with cardinality , ie, mandatory feature f solitary feature with cardinality , ie, optional feature f grouped. Likely to invest a lot of time, research, effort, and mental energy into coming to the right conclusion so how exactly does this process work the following are some of the major decision-making strategies that you might use: the single-feature model this approach involves hinging your decision solely on a single-feature. The precise feature representation that the brain uses when making decisions, including the particular distribution of feature vectors, is largely unknown consequently, we take a suitably parsimonious approach and model (abstract) feature vectors as samples from one of two gaussian distributions which. It is one of the predictive modelling approaches used in statistics, data mining and machine learning tree models where the target variable can take a discrete set of values are called classification trees in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those.
Described eg with feature models, or with a domain specific language (dsl) there are unfortunately, no single standard has yet been agreed for the graphical notation of feature models however, in development, since many architectural and implementation decisions can be made on the basis of the variabilities. Describe which parts of the reference architecture imple- ment a single feature with this knowledge in mind it is possible to trace decisions on the level of the feature model down to relevant components of the architecture in contrast, it is also possible to exclude parts of the architec- ture based on the decision not to include. Software is high one way to reduce this risk is provided by domain analysis concepts, properties and solutions of a domain are analyzed based on this information decisions about software development for future applications within such a domain are made as part of domain analysis methods, feature models are used. Feature models have been cited as one of the main con- tributions to model software product families however, there is still a gap in product family engineering which is the automated reasoning on feature models in this paper we describe how to reason on feature models using con- straint programming although, there.