A Testing Approach for XML Schemas

Abstract

XML is a language frequently used for data representation and interchange in Web-based applications. In most cases, the XML documents must conform to a schema that defines the type of data that is accepted by a web application. In this sense, an error in the schema or in the XML document can lead to failures in the application; the use of testing approaches, criteria and specific tools to ensure the reliability of data in the XML format is fundamental. We present a testing approach that helps to reveal faults in XML schemas. The test process involves generating XML documents with some modifications with respect to the original XML document and using queries to these documents to validate the schema. The XML documents and queries are generated according to a set of fault classes defined for the XML schemas. A case study applying the proposed approach is described and the results are presented.

XTool: Uma Ferramenta de Teste Baseado em Defeitos para Esquemas de Dados

“…Além desses estudos de caso foram realizados outros dois, nos quais os defeitos foram semeados nos esquemas em teste, gerando esquemas mutantes. Um dos estudos de caso utilizou a ferramenta XTM para gerar diferentes esquemas XML mutantes incorretos (Emer et al 2007b). Esses esquemas foram testados com a XTool e todos tiveram seus defeitos revelados, mostrando que as classes de defeitos da XTool cobrem os defeitos descritos pelos operadores de mutação da XTM.…”

“…A XTool (XML and Relational Database Schema Testing Tool) apóia o teste de esquemas de dados implementando uma abordagem baseada em defeitos, denominada Análise de Instâncias de Dados Alternativas (AIDA) (Emer et al 2007a). Essa abordagem tem como objetivo detectar defeitos em esquemas de dados para garantir a integridade dos dados definidos por meio do esquema e manipulados por uma aplicação de software.…”

“…Essa abordagem é baseada em defeitos porque considera classes de defeitos comumente presentes em esquemas. É genérica porque está baseada em um modelo formal que permite a representação de diferentes tipos de esquemas (Emer et al 2007a). Na abordagem de teste AIDA, uma instância de dados associada ao esquema em teste sofre alterações simples gerando instâncias de dados alternativas.…”

A Critical Review for Trustworthy and Explainable Structural Health Monitoring and Risk Prognosis of Bridges with Human-In-The-Loop

“…Incorrect external keys for representing the related columns in two tables and linking the information from two tables [150]; redundant information items having inconsistent values at different parts of the database [151,152]; missing relationships between two tables while the link should exist for linking common columns in two tables [153,154] Sensory Data Errors or missing values in time series sensory data that measures structure vibrations [155] Metadata Errors in the metadata for specifying the formats and organization of datasets, such as the meaning of columns of numbers in a data file [144]; errors in the metadata for specifying the time and data collection environments [156]; errors in the metadata for specifying the methods of processing and transforming the data, such as a transformation matrix for transforming point clouds to a global coordinate system [157] Table 5 indicates that data reliability issues have attracted more attention from the domain of civil engineering. Some researchers examined the quality of 3D imagery data in terms of accuracy, level of comprehensiveness, and detail [132,133].…”

“…Incorrect external keys for representing the related columns in two tables and linking the information from two tables [150]; redundant information items having inconsistent values at different parts of the database [151,152]; missing relationships between two tables while the link should exist for linking common columns in two tables [153,154] Sensory Data Errors or missing values in time series sensory data that measures structure vibrations [155] Metadata Errors in the metadata for specifying the formats and organization of datasets, such as the meaning of columns of numbers in a data file [144]; errors in the metadata for specifying the time and data collection environments [156]; errors in the metadata for specifying the methods of processing and transforming the data, such as a transformation matrix for transforming point clouds to a global coordinate system [157] Model 2D/3D Maps Location errors of points [158]; length and direction errors of lines representing paths on 2D or 3D maps [159]; level of detail of maps [160]; missing values in the properties of objects on 2D or 3D maps [161] Semantic-Rich Digital Models Missing and additional objects [162]; dimensional and shape deviations from actual dimensions [163,164]; wrong type information of objects [165] These data-quality studies still have not yet addressed some challenges related to quantifying the data and model reliability in engineering application contexts. Compared with systematic quality quantification of structured data and images, one challenge is that relatively limited studies examined metrics for measuring the quality of audio, video, and natural language reports (Taleb et al 2018 [166]).…”