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

Shah, Sejal; Sudarshan, S.; Kajbaje, Suhas; Patidar, Sandeep; Gupta, Bhanu Pratap; Vira, Devang

doi:10.1109/icde.2011.5767876

Cited by 33 publications

(51 citation statements)

References 28 publications

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“…In Tuya et al (2010), Cabal and Riva developed a method of generating records to satisfy the criterion developed in de la . In Gupta et al (2010) and Shah et al (2011), generating test data for killing SQL mutants was investigated. However, these approaches do not consider program constraints and cannot deal with database applications directly.…”

Section: Related Workmentioning

confidence: 99%

Program-input generation for testing database applications using existing database states

Pan

Xie

2014

Autom Softw Eng

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Testing is essential for quality assurance of database applications. Achieving high code coverage of the database applications is important in testing. In practice, there may exist a copy of live databases that can be used for database application testing. Using an existing database state is desirable since it tends to be representative of real-world objects' characteristics, helping detect faults that could cause failures in real-world settings. However, to cover a specific program-code portion (e.g., block), appropriate program inputs also need to be generated for the given existing database state. To address this issue, in this paper, we propose a novel approach that generates program inputs for achieving high code coverage of a database application, given an existing database state. Our approach uses symbolic execution to track how program inputs are transformed before appearing in the executed SQL queries and how the constraints on query results affect the application's execution. One significant challenge in our problem context is the gap between program-input constraints derived from the program and from the given existing database state; satisfying both types of constraints is needed to cover a specific program-code portion. Our approach includes novel query 123 Autom Softw Eng formulation to bridge this gap. We incorporate the data-instantiation component in our framework to deal with the case that no effective program input values can be attained. We determine how to generate new records and populate them in the new database state such that the code along the path can be covered. We also extend our approach of program-input generation to test database applications including canonical queries and group-by queries. Our approach is loosely integrated into Pex, a state-of-the-art white-box testing tool for .NET from Microsoft Research. Empirical evaluations on two real database applications show that our approach assists Pex to generate program inputs that achieve higher code coverage than the program inputs generated by Pex without our approach's assistance.

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Section: Related Workmentioning

confidence: 99%

Program-input generation for testing database applications using existing database states

Pan

Xie

2014

Autom Softw Eng

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“…With the goal to test SQL queries, some works address the test database generation by imposing constraints on the database statements using different test criteria, such as predicate coverage [50], [51], [52], mutation coverage [53], [54] or the SQLFpc criterion [55]. Constraint-based approaches are also explored to test database programs.…”

Section: Testing 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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“…Automated generation of datasets designed to catch errors in specific queries is therefore an area of importance. The XData system [2], [3], [4] developed at IIT Bombay, generates dataset(s) that can catch commonly occurring errors in a large class of SQL queries using an approach based on query mutations.…”

Section: Introductionmentioning

confidence: 99%

The XDa-TA system for automated grading of SQL query assignments

Bhangdiya

Chandra

Kar

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Grading of student SQL queries is usually done by executing the query on sample datasets (which may be unable to catch many errors) and/or by manually comparing/checking a student query with the correct query (which can be tedious and error prone). In this demonstration we present the XDa-TA system which can be used by instructors and TAs for grading SQL query assignments automatically. Given one or more correct queries for an SQL assignment, the tool uses the XData system to automatically generate datasets that are designed specifically to catch common errors. The grading is then done by comparing the results of student queries with those of the correct queries against these generated datasets; instructors can optionally provide additional datasets for testing. The tool can also be used in a learning mode by students, where it can provide immediate feedback with hints explaining possible reasons for erroneous output. This tool could be of great value to instructors particularly, to instructors of MOOCs.

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

Cited by 33 publications

References 28 publications

Program-input generation for testing database applications using existing database states

Program-input generation for testing database applications using existing database states

Coverage-Aware Test Database Reduction

The XDa-TA system for automated grading of SQL query assignments

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