Sql Schema for Test Data
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8 the Effectiveness of Test Coverage Criteria for Relational Database Schema Integrity Constraints
The Effectiveness of Test Coverage Criteria for Relational Database Schema Integrity Constraints PHIL MCMINN and CHRIS J. WRIGHT, University of Sheffield GREGORY M. KAPFHAMMER, Allegheny College Despite industry advice to the contrary, there has been little work that has sought to test that a relational database’s schema has correctly specified integrity constraints. These critically important constraints ensure the coherence of data in a database, defending it from manipulations that could violate requirements such as “usernames must be unique” or “the host name cannot be missing or unknown.” This article is the first to propose coverage criteria, derived from logic coverage criteria, that establish different levels of testing for the formulation of integrity constraints in a database schema. These range from simple criteria that mandate the testing of successful and unsuccessful INSERT statements into tables to more advanced criteria that test the formulation of complex integrity constraints such as multi-column PRIMARY KEYs and arbitrary CHECK constraints. Due to different vendor interpretations of the structured query language (SQL) specification with regard to how integrity constraints should actually function in practice, our criteria crucially account for the underlying semantics of the database management system (DBMS). After formally defining these coverage criteria and relating them in a subsumption hierarchy, we present two approaches for automatically generating tests that satisfy the criteria. We then describe the results of an empirical study that uses mutation analysis to investigate the fault-finding capability of data generated when our coverage criteria are applied to a wide variety of relational schemas hosted by three well-known and representative DBMSs— HyperSQL, PostgreSQL, and SQLite. -
Testing Data Vault-Based Data Warehouse
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses and Dissertations Graduate Studies, Jack N. Averitt College of Fall 2015 Testing Data Vault-Based Data Warehouse Connard N. Williams Follow this and additional works at: https://digitalcommons.georgiasouthern.edu/etd Part of the Computer and Systems Architecture Commons, Databases and Information Systems Commons, Data Storage Systems Commons, and the Software Engineering Commons Recommended Citation Williams, Connard N., "Testing Data Vault-Based Data Warehouse" (2015). Electronic Theses and Dissertations. 1340. https://digitalcommons.georgiasouthern.edu/etd/1340 This thesis (open access) is brought to you for free and open access by the Graduate Studies, Jack N. Averitt College of at Digital Commons@Georgia Southern. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons@Georgia Southern. For more information, please contact [email protected]. 1 TESTING DATA VAULT-BASED DATA WAREHOUSE by CONNARD N. WILLIAMS (Under the Direction of Vladan Jovanovic) ABSTRACT Data warehouse (DW) projects are undertakings that require integration of disparate sources of data, a well-defined mapping of the source data to the reconciled data, and effective Extract, Transform, and Load (ETL) processes. Owing to the complexity of data warehouse projects, great emphasis must be placed on an agile-based approach with properly developed and executed test plans throughout the various stages of designing, developing, and implementing the data warehouse to mitigate against budget overruns, missed deadlines, low customer satisfaction, and outright project failures. Yet, there are often attempts to test the data warehouse exactly like traditional back-end databases and legacy applications, or to downplay the role of quality assurance (QA) and testing, which only serve to fuel the frustration and mistrust of data warehouse and business intelligence (BI) systems. -
Preventing Data Errors with Continuous Testing
Preventing Data Errors with Continuous Testing Kıvanç Mu¸slu Yuriy Brun Alexandra Meliou Computer Science & Engineering College of Information and Computer Science University of Washington University of Massachusetts Seattle, WA, USA 98195-2350 Amherst, MA, USA 01003-9264 [email protected] {brun, ameli}@cs.umass.edu ABSTRACT Data errors can arise in a variety of ways, including during data Today, software systems that rely on data are ubiquitous, and en- entry (e.g., typographical errors and transcription errors from il- suring the data’s quality is an increasingly important challenge as legible text), measurement (e.g., the data source may be faulty or data errors result in annual multi-billion dollar losses. While soft- corrupted), and data integration [33]. Data entry errors have been ware debugging and testing have received heavy research attention, found hard to detect and to significantly alter conclusions drawn on less effort has been devoted to data debugging: identifying system that data [3]. The ubiquity of data errors [21, 33, 75, 76] has resulted errors caused by well-formed but incorrect data. We present con- in significant research on data cleaning [13, 20, 38, 67, 69, 77] and tinuous data testing (CDT), a low-overhead, delay-free technique data debugging [4, 37]. Such work, focusing on identifying likely that quickly identifies likely data errors. CDT continuously executes data errors, inconsistencies, and statistical outliers can help improve domain-specific test queries; when a test fails, CDT unobtrusively the quality of a noisy or faulty data set. However, with few notable exceptions [15, 47], research has not focused on the problem of warns the user or administrator. -
Preview Database Testing Tutorial
Database Testing About the Tutorial Database testing includes performing data validity, data integrity testing, performance check related to database and testing of procedures, triggers and functions in the database. This is an introductory tutorial that explains all the fundamentals of Database testing. Audience This tutorial has been designed for all those readers who want to learn the basics of Database testing. It is especially going to be useful for all those software testing professionals who are required to test the applications in order to find out how they affect the database performance. Prerequisites We assume the readers of this tutorial have hands-on experience of handling a database using SQL queries. In addition, it is going to help if the readers have an elementary knowledge of basic database concepts. Copyright & Disclaimer Copyright 2016 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at [email protected] i Database Testing Table of Contents About the Tutorial ...........................................................................................................................................