An AGENDA for testing relational database applications

Chays, David; Deng, Yuetang; Frankl, Phyllis G.; Dan, Saikat; Vokolos, Filippos I.; Weyuker, Elaine J.

doi:10.1002/stvr.286

Cited by 96 publications

(69 citation statements)

References 24 publications

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“…On a related but complementary level, a substantial amount of work (e.g. [44,45,46,47,33,48,49,50,51,52,20,53,54,55,56]) has been done on how to generate test database content exhibiting some desirable properties, given only the database schema and possibly some queries to be executed over the database. The main difference between our work and these approaches is that they essentially work without considering the control flow of the programs manipulating the database.…”

Section: Other Related Workmentioning

confidence: 99%

Relational symbolic execution of SQL code for unit testing of database programs

Marcozzi

Vanhoof

Hainaut

2015

Science of Computer Programming

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Symbolic execution is a technique enabling the automatic generation of test inputs that exercise a set of execution paths within a code unit to be tested. If the paths cover a sufficient part of the code under test, the test data offer a representative view of the actual behaviour of this code. This notably enables detecting errors and correcting faults. Relational databases are ubiquitous in software, but symbolic execution of code units that manipulate them remains a non-trivial problem, particularly because of the complex structure of such databases and the complex behaviour of SQL statements. Finding errors in such code units is yet critical, as it can avoid corrupting important data. In this work, we define a symbolic execution translating database manipulation code directly into constraints and integrate it with a more traditional symbolic execution of normal program code. The database tables are represented by relational symbols and the SQL statements by relational constraints over these symbols. An algorithm based on these principles is presented for the symbolic execution of simple Java methods that implement transactional use cases by reading and writing in a relational database, the latter subject to data integrity constraints. The algorithm is integrated in a test generation tool and experimented over sample code. The target language for the constraints produced by the tool is the SMT-Lib standard and the used solver is Microsoft Z3. The results show that the proposed approach enables generating meaningful test data, including valid database content, in reasonable time. In particular, the Z3 solver is shown to be more scalable than the Alloy solver, used in our previous work, for solving relational constraints.

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Section: Other Related Workmentioning

confidence: 99%

Relational symbolic execution of SQL code for unit testing of database programs

Marcozzi

Vanhoof

Hainaut

2015

Science of Computer Programming

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“…Prior approaches to automated generation of datasets such as AGENDA [10], RQP [3] and Olston et al [24], generate datasets which ensure that the results of specified queries are non-empty. However, they do not address mutations, and there is no guarantee of completeness of the test cases generated.…”

Section: Select * From Instructor I Left Outer Join Teaches T On (Iimentioning

confidence: 99%

“…RELATED WORK The AGENDA toolset for testing database applications [10] has several components, including a component for generating a test database for a given query. However, [10] does not address mutation testing, and does not provide any guarantees about completeness of the test data that it generates.…”

Section: Space Of Queries and Mutants Consideredmentioning

confidence: 99%

Generating test data for killing SQL mutants: A constraint-based approach

Shah

Sudarshan

Kajbaje

et al. 2011

2011 IEEE 27th International Conference on Data Engineering

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Abstract-Complex SQL queries are widely used today, but it is rather difficult to check if a complex query has been written correctly. Formal verification based on comparing a specification with an implementation is not applicable, since SQL queries are essentially a specification without any implementation. Queries are usually checked by running them on sample datasets and checking that the correct result is returned; there is no guarantee that all possible errors are detected.In this paper, we address the problem of test data generation for checking correctness of SQL queries, based on the query mutation approach for modeling errors. Our presentation focuses in particular on a class of join/outer-join mutations, comparison operator mutations, and aggregation operation mutations, which are a common cause of error. To minimize human effort in testing, our techniques generate a test suite containing small and intuitive test datasets. The number of datasets generated, is linear in the size of the query, although the number of mutations in the class we consider is exponential. Under certain assumptions on constraints and query constructs, the test suite we generate is complete for a subclass of mutations that we define, i.e., it kills all non-equivalent mutations in this subclass.

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“…Some tools (AGENDA [7], HTDGen [8]) support the generation of data not only on the basis of constraints defined by schema or user, but also based on SQL queries of database application under test. It allows generating such data that SQL queries of an implementation under test return some meaningful results.…”

Section: Tools Reviewmentioning

confidence: 99%

Test Data Generation for Covering Functionality of Database Applications

Kostychev¹,

Омельченко²,

Zelenov³

2010

Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering

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Abstract-Applications for processing great volumes of data is a very widely used kind of software. In enterprise integration there are tasks of data integration. When solving these tasks, special tools supporting development and execution of applications implementing extract, transformation and load pattern are often used. From the point of view of functional testing, such applications have a specific peculiarity related to a huge number of combinations of input data. Existing approaches and tools solving the problem of test data generation for database application build large arrays of input data based on database scheme or on SQL queries of application under tests. To ensure covering functionality of an application under test using these approaches and tools, a brute force of all available combinations is needed. In the paper, we prpose a method allowing less excessive data generation for covering functionality of database applications. It allows achieving functionality coverage with acceptable amount of test data close to optimal one (one test per one functionality branch) in acceptable generation time.

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An AGENDA for testing relational database applications

Cited by 96 publications

References 24 publications

Relational symbolic execution of SQL code for unit testing of database programs

Relational symbolic execution of SQL code for unit testing of database programs

Generating test data for killing SQL mutants: A constraint-based approach

Test Data Generation for Covering Functionality of Database Applications

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