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Full predicate coverage for testing SQL database queries.

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Τίτλος: Full predicate coverage for testing SQL database queries.
Συγγραφείς: Tuya, Javier, Suárez-Cabal, María José, de la Riva, Claudio
Πηγή: Software Testing: Verification & Reliability; Sep2010, Vol. 20 Issue 3, p237-288, 52p
Θεματικοί όροι: Semantic network analysis, Criterion (Theory of knowledge), Database management software, Syntax (Grammar), PL/SQL (Computer program language)
Περίληψη: In the field of database applications a considerable part of the business logic is implemented using a semi-declarative language: the Structured Query Language (SQL). Because of the different semantics of SQL compared with other procedural languages, the conventional coverage criteria for testing are not directly applicable. This paper presents a criterion specifically tailored for SQL queries (SQLFpc). It is based on Masking Modified Condition Decision Coverage (MCDC) or Full Predicate Coverage and takes into account a wide range of the syntax and semantics of SQL, including selection, joining, grouping, aggregations, subqueries, case expressions and null values. The criterion assesses the coverage of the test data in relation to the query that is executed and it is expressed as a set of rules that are automatically generated and efficiently evaluated against a test database. The use of the criterion is illustrated in a case study, which includes complex queries. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Copyright of Software Testing: Verification & Reliability is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Full predicate coverage for testing SQL database queries.
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  Data: Software Testing: Verification & Reliability; Sep2010, Vol. 20 Issue 3, p237-288, 52p
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  Data: <searchLink fieldCode="DE" term="%22Semantic+network+analysis%22">Semantic network analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Criterion+%28Theory+of+knowledge%29%22">Criterion (Theory of knowledge)</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management+software%22">Database management software</searchLink><br /><searchLink fieldCode="DE" term="%22Syntax+%28Grammar%29%22">Syntax (Grammar)</searchLink><br /><searchLink fieldCode="DE" term="%22PL%2FSQL+%28Computer+program+language%29%22">PL/SQL (Computer program language)</searchLink>
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  Data: In the field of database applications a considerable part of the business logic is implemented using a semi-declarative language: the Structured Query Language (SQL). Because of the different semantics of SQL compared with other procedural languages, the conventional coverage criteria for testing are not directly applicable. This paper presents a criterion specifically tailored for SQL queries (SQLFpc). It is based on Masking Modified Condition Decision Coverage (MCDC) or Full Predicate Coverage and takes into account a wide range of the syntax and semantics of SQL, including selection, joining, grouping, aggregations, subqueries, case expressions and null values. The criterion assesses the coverage of the test data in relation to the query that is executed and it is expressed as a set of rules that are automatically generated and efficiently evaluated against a test database. The use of the criterion is illustrated in a case study, which includes complex queries. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Software Testing: Verification & Reliability is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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