Inferential Checking for Mutants Modifying Database States

Zhou, Chixiang; Frankl, Phyllis G.

doi:10.1109/icst.2011.63

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…Our earlier work using only mutants of SELECT statements [1] demonstrated similar results. In addition, in order to visualize the relationship among the three variables, we drew a series of side-by-side three-dimensional scatter plots on the two versions of data sets, shown in Figure 2 7 . The horizontal axes represent size and coverage and the vertical axis represents effectiveness (mutation score).…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…We subsequently developed JDAMA to apply their SQL mutation in the context of Java/JDBC programs [6], [1]. In addition, we proposed a new approach inferential checking [7] to incorporating updating commands (UPDATE, DELETE, and INSERT) for Java database applications. In this paper, we employed these related papers to aid our experiments.…”

Section: Test Effectiveness Of An Adequacy Criterionmentioning

confidence: 99%

“…We applied it in the context of Java database applications previously [6], [7] to compare different test generation techniques for Java/JDBC programs. Database applications are often multilayered and we target the middle tier where the SQL queries are constructed and application logic is implemented.…”

Section: Background a Mutation Testing For Database Applicationsmentioning

confidence: 99%

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Empirical Studies on Test Effectiveness for Database Applications

Zhou

Frankl

2012

2012 IEEE Fifth International Conference on Software Testing, Verification and Validation

Self Cite

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This paper presents empirical studies of test effectiveness for Java database applications. We primarily address two research questions: (1) What is the relationship between test set size, coverage criteria, and test effectiveness for Java/JDBC programs? (2) Which adequacy criterion is more suitable for testing Java database applications, statement coverage, branch coverage, or command form coverage? We collected data sets by experimenting on several moderately complex open source database application programs, and performed a series of rigorous and comprehensive statistical analyses. The results show that linear models fit the relationship of size and coverage to effectiveness well and that coverage appears to have greater impact on effectiveness than size. However, surprisingly, command form coverage was not consistently more effective than statement or branch coverage.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Section: Test Effectiveness Of An Adequacy Criterionmentioning

confidence: 99%

Section: Background a Mutation Testing For Database Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Studies on Test Effectiveness for Database Applications

Zhou

Frankl

2012

2012 IEEE Fifth International Conference on Software Testing, Verification and Validation

Self Cite

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“…In the fault-based category the existing works range from the development of sets of mutants for SQL queries [17], [18] or schemas [19], [20], [21] to the evaluation of the fault-detection effectiveness with tools [22], [23], [24], [25], fault-localization [26] and empirical studies [27]. Others are application specific, mainly with the goal of detecting SQL injection vulnerabilities [28], [29], [30] and preventing them [31].…”

Section: Test Coverage For Database Applicationsmentioning

confidence: 99%

Coverage-Aware Test Database Reduction

Tuya

Riva

Suárez-Cabal

et al. 2016

IIEEE Trans. Software Eng.

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Abstract-Functional testing of applications that process the information stored in databases often requires a careful design of the test database. The larger the test database, the more difficult it is to develop and maintain tests as well as to load and reset the test data. This paper presents an approach to reduce a database with respect to a set of SQL queries and a coverage criterion. The reduction procedures search the rows in the initial database that contribute to the coverage in order to find a representative subset that satisfies the same coverage as the initial database. The approach is automated and efficiently executed against large databases and complex queries. The evaluation is carried out over two real life applications and a well-known database benchmark. The results show a very large degree of reduction as well as scalability in relation to the size of the initial database and the time needed to perform the reduction.

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“…JDAMA currently only handles SQL SELECT statements. Recent research on extending it to handle SQL statements that modify database state (UPDATE, DELETE, and INSERT) is described in the paper of Zhou et al 28. The implementation of the instrumenter extends a Soot abstract class BodyTransformer.…”

Section: Jdama Design and Implementationmentioning

confidence: 99%

JDAMA: Java database application mutation analyser

Zhou

Frankl

2011

Software Testing Verif & Rel

Self Cite

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Database application programs are ubiquitous; hence, good techniques for testing them are needed. Several approaches to testing database applications have been proposed recently, including test generation techniques and adequacy criteria. Tuya et al. proposed mutation‐based adequacy criteria for database queries and developed a set of mutation operators for Structured Query Language SELECT statements. This paper extends that approach by integrating it with analysis and instrumentation of the application bytecode. The design and implementation of a tool, Java Database Application Mutation Analyser (JDAMA), are described. JDAMA instruments application bytecode so as to compare the results of queries executed by the application under test to the results of mutants of those queries. JDAMA can be used to support mutation testing for Java database applications. A second, potentially more useful application of JDAMA is to support experiments evaluating testing techniques for database applications, in which query mutation is used to seed faults into subject applications. The paper illustrates these uses of JDAMA with experiments comparing two approaches to test generation and experiments evaluating coverage criteria. Copyright © 2011 John Wiley & Sons, Ltd.

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Inferential Checking for Mutants Modifying Database States

Cited by 7 publications

References 22 publications

Empirical Studies on Test Effectiveness for Database Applications

Empirical Studies on Test Effectiveness for Database Applications

Coverage-Aware Test Database Reduction

JDAMA: Java database application mutation analyser

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