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3 results on '"CONFORMANCE CHECKS"'

1. Evaluation of Markov Models for Architecture Conformance Checking.

Author

Rodriguez, Guillermo, Armentano, Marcelo, Soria, Alvaro, and Corengia, Emilio

Abstract

Conformance between architecture and implementation is a key aspect of architecture-centric development. However, the architecture "as documented" and the architecture "as implemented" tend to diverge from each other over time. Thus, conformance checks should be run periodically on the system in order to detect and correct differences. Despite having a structural conformance analysis, assessing whether the main scenarios describing the architectural behavior are faithfully implemented in the code is still challenging. Checking conformance to architectural scenarios is usually a time-consuming and error-prone activity. In this article, we describe ArchLearner, a tool to assist architects to bridge the gap between architecture and its implementation. The architecture is specified with Use-Case Maps (UCMs), a notation for modeling both high-level structure and behavior. ArchLearner uses Markov Models to detect code deviations with respect to predetermined UCMs, based on the analysis of system execution traces for those UCMs. The results from two case-studies have shown that ArchLearner is practical and reduces conformance checking efforts. [ABSTRACT FROM AUTHOR]

Published
2020
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2. A Data Quality Approach to Conformance Checks for Business Network Models.

Author

Ritter, Daniel and Rupprich, Stephanie

Abstract

The recent advances in programming languages, compilers and hardware allow software systems to process huge amounts of data. Therefore data is represented in domain specific models, capturing real-world artifacts as structured data for calculation and analysis. To guarantee data quality and reduce uncertainty within the domain information, model and content-based data conformance have to be checked. Domain model changes often require code changes for validations and redeployment to keep the pace. Current programming techniques and tools do not or only partially address this topic. In this paper, we present a novel approach to content-based validation of structured data for arbitrary domains. We define a validation programming model and show how a compiler and run-time system based on Deterministic Finite Automata(DFA) can be generated. We validated this approach by applying it to the domain of Network Mining (NM), which requires conformance checks for discovered raw data, used to compute business networks. The validation programs are illustrated by debugging the run-time system with textual and graphical tools. [ABSTRACT FROM PUBLISHER]

Published
2012
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3. Evaluation of Markov Models for Architecture Conformance Checking

Author

Rodríguez, Guillermo Horacio, Armentano, Marcelo Gabriel, Soria, Alvaro, and Corengia, Emilio

Subjects
purl.org/becyt/ford/1 [https], SOFTWARE ARCHITECTURE, VARIABLE ORDER MARKOV MODELS, CONFORMANCE CHECKS, Ciencias de la Computación e Información, purl.org/becyt/ford/1.2 [https], TOOL SUPPORT, CIENCIAS NATURALES Y EXACTAS, USE CASE MAPS, Ciencias de la Computación
Abstract

Conformance between architecture and implementation is a key aspect of architecture-centric development. However, the architecture as documented and the architecture as implemented tend to diverge from each other over time. Thus, conformance checks should be run periodically on the system in order to detect and correct differences. Despite having a structural conformance analysis, assessing whether the main scenarios describing the architectural behavior are faithfully implemented in the code is still challenging. Checking conformance to architectural scenarios is usually a time-consuming and error-prone activity. In this article, we describe ArchLearner, a tool to assist architects to bridge the gap between architecture and its implementation. The architecture is specified with Use-Case Maps (UCMs), a notation for modeling both high-level structure and behavior. ArchLearner uses Markov Models to detect code deviations with respect to predetermined UCMs, based on the analysis of system execution traces for those UCMs. The results from two case-studies have shown that ArchLearner is practical and reduces conformance checking efforts. Fil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Soria, Alvaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Corengia, Emilio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina

Published
2020

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