A logic mutation approach to selective mutation for programs and queries

Kaminski, Garrett Kent; Praphamontripong, Upsorn; Ammann, Paul; Offutt, Jeff

doi:10.1016/j.infsof.2011.03.009

Cited by 24 publications

(15 citation statements)

References 44 publications

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“…Wright's dissertation presented a unified treatment of these approaches to efficient schema mutation testing [47]. While this dissertation, and the author's aforementioned work (e.g., [12], [14], [15]), focused on mutating the CREATE TABLE statements that produce the schema, other prior work has proposed mutation operators for the SQL SELECT statements used by applications to retrieve data stored in a database [46], [79]. The idea of mutation analysis for database queries was later incorporated into a tool for instrumenting and testing database applications written in the Java programming language, potentially mutating any executed SELECT statement [80].…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas

McMinn

Wright

McCurdy

et al. 2019

IIEEE Trans. Software Eng.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas

McMinn

Wright

McCurdy

et al. 2019

IIEEE Trans. Software Eng.

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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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“…The approach was also applied to programs and queries [15], as well as at the system level based on Java programs [12]. To the best of our knowledge, it has not been studied for dynamically typed languages, including Python.…”

Section: B Higher Order Mutationmentioning

confidence: 99%

Experimental Evaluation of Mutation Testing Approaches to Python Programs

Derezińska

Hałas

2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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Mutation testing of Python programs raises a problem of incompetent mutants. Incompetent mutants cause execution errors due to inconsistency of types that cannot be resolved before run-time. We present a practical approach in which incompetent mutants can be generated, but the solution is transparent for a user and incompetent mutants are detected by a mutation system during test execution. Experiments with 20 traditional and object-oriented operators confirmed that the overhead can be accepted. The paper presents an experimental evaluation of the first-and higher-order mutation. Four algorithms to the 2nd and 3rd order mutant generation were applied. The impact of code coverage consideration on the process efficiency is discussed. The experiments were supported by the MutPy system for mutation testing of Python programs.

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A logic mutation approach to selective mutation for programs and queries

Cited by 24 publications

References 44 publications

Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas

Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas

Coverage-Aware Test Database Reduction

Experimental Evaluation of Mutation Testing Approaches to Python Programs

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