Grammatically Interpreting Feature Compositions
نویسندگان
چکیده
Feature modeling is a popular domain analysis method for describing the commonality and variability among the domain products. The current formalisms of feature modelling do not have enough support for automated domain product configuration and validation. We have developed a theory of feature modeling: a feature model is analogous to a definition of a language; a particular feature composition instance (domain product) is analogous to a program written in that language; and the way the features can be assembled to form a product is analogous to the way various tokens can be assembled to form a program. To apply this theory, we have developed a meta-language Two-Level Grammar++ to specify feature models. The interpreter derived from the feature model specification performs automated product configuration and product quality validation.
منابع مشابه
The role of feature-number and feature-type in processing Hindi verb agreement violations.
This article presents studies of Hindi that investigate whether responses to syntactic agreement violations vary as a function of the type and number of incorrect agreement features, using both electrophysiological (ERP) and behavioral measures. Hindi is well suited to investigation of this issue, since verbs in Hindi mark agreement with the person, number, and gender features of the nominative...
متن کاملA longitudinal study of sentence comprehension difficulty in primary progressive aphasia.
CONTEXT Patients with primary progressive aphasia have sentence comprehension difficulty, but the longitudinal course of this deficit has not been investigated. OBJECTIVE To determine how grammatical, single word meaning, and working memory factors contribute to longitudinal decline of sentence comprehension in primary progressive aphasia. We hypothesised partially distinct patterns of senten...
متن کاملA Rational Analysis of Rule-Based Concept Learning
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space-a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and fin...
متن کاملValidating Component Compositions in Software System Generators1
Generators synthesize software systems by composing components from reuse libraries. In general, not all syntactically correct compositions are semantically correct. In this paper, we present domain-independent algorithms for the GenVoca model of software system generation to validate component compositions. Our work relies on attribute grammars and offers powerful debugging capabilities with e...
متن کاملValidating component compositions in software system generators
Generators synthesize software systems by composing components from reuse libraries. In general, not all syntactically correct compositions are semantically correct. In this paper, we present domain-independent algorithms for the GenVoca model of software generators to validate component compositions. Our work relies on attribute grammars and offers powerful debugging capabilities with explanat...
متن کامل