[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/2896921.2896933acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article

Virtual mutation analysis of relational database schemas

Published: 14 May 2016 Publication History

Abstract

Relational databases are a vital component of many modern software applications. Key to the definition of the database schema --- which specifies what types of data will be stored in the database and the structure in which the data is to be organized --- are integrity constraints. Integrity constraints are conditions that protect and preserve the consistency and validity of data in the database, preventing data values that violate their rules from being admitted into database tables. They encode logic about the application concerned, and like any other component of a software application, need to be properly tested. Mutation analysis is a technique that has been successfully applied to integrity constraint testing, seeding database schema faults of both omission and commission. Yet, as for traditional mutation analysis for program testing, it is costly to perform, since the test suite under analysis needs to be run against each individual mutant to establish whether or not it exposes the fault. One overhead incurred by database schema mutation is the cost of communicating with the database management system (DBMS). In this paper, we seek to eliminate this cost by performing mutation analysis virtually on a local model of the DBMS, rather than on an actual, running instance hosting a real database. We present an empirical evaluation of our virtual technique revealing that, across all of the studied DBMSs and schemas, the virtual method yields an average time saving of 51% over the baseline.

References

[1]
Y. Abrahami. Scaling to 100M: MySQL is a better NoSQL. http://goo.gl/Q9fW5P. (Accessed 11/12/2015).
[2]
B. Butler. Amazon: Our cloud powered Obama's campaign. Net. Wor., 2012.
[3]
E. F. Codd. A relational model of data for large shared data banks. CACM, 13, 1970.
[4]
S. Guz. Basic mistakes in database testing. http://goo.gl/ByifeQ. (Accessed 24/01/2014).
[5]
L. Inozemtseva and R. Holmes. Coverage is not strongly correlated with test suite effectiveness. In Proc. of 36th ICSE, 2014.
[6]
Y. Jia and M. Harman. An analysis and survey of the development of mutation testing. TSE, 37(5), 2011.
[7]
R. Just, G. M. Kapfhammer, and F. Schweiggert. Using conditional mutation to increase the efficiency of mutation analysis. In Proc. of 6th AST, 2011.
[8]
G. M. Kapfhammer. A Comprehensive Framework for Testing Database-Centric Applications. PhD thesis, University of Pittsburgh, 2007.
[9]
G. M. Kapfhammer, P. McMinn, and C. J. Wright. Search-based testing of relational schema integrity constraints across multiple database management systems. In Proc. of 6th ICST, 2013.
[10]
A. McLeod. Kendall: Kendall rank correlation and Mann-Kendall trend test, 2011. R package version 2.2.
[11]
P. McMinn, C. J. Wright, and G. M. Kapfhammer. The effectiveness of test coverage criteria for relational database schema integrity constraints. TOSEM, 25(1), 2015.
[12]
G. Neumann, M. Harman, and S. Poulding. Transformed Vargha-Delaney effect size. In Proc. of 7th SBSE. 2015.
[13]
A. Offutt and R. Untch. Mutation 2000: Uniting the orthogonal. Proc. of Mutation, 2001.
[14]
S. Tokumoto, H. Yoshida, K. Sakamoto, andS. Honiden. MuVM: Higher order mutation analysis virtual machine for C. In Proc. of 9th ICST, 2016.
[15]
J. Tuya, M. J. Suárez-Cabal, and C. de la Riva. Mutating database queries. IST, 49(4), 2006.
[16]
A. Vargha and H. D. Delaney. A critique and improvement of the CL common language effect size statistics of McGraw and Wong. EBS, 25(2), 2000.
[17]
C. J. Wright, G. M. Kapfhammer, and P. McMinn. Efficient mutation analysis of relational database structure using mutant schemata and parallelisation. In Proc. of 8th Mutation, 2013.
[18]
C. J. Wright, G. M. Kapfhammer, and P. McMinn. The impact of equivalent, redundant and quasi mutants on database schema mutation analysis. In Proc. of 14th QSIC, 2014.

Cited By

View all
  • (2020)STICCER: Fast and Effective Database Test Suite Reduction Through Merging of Similar Test Cases2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)10.1109/ICST46399.2020.00031(220-230)Online publication date: Oct-2020
  • (2019)Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database SchemasIEEE Transactions on Software Engineering10.1109/TSE.2017.278628645:5(427-463)Online publication date: 1-May-2019
  • (2018)A Systematic Review of Cost Reduction Techniques for Mutation Testing: Preliminary Results2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW.2018.00021(1-10)Online publication date: Apr-2018
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
AST '16: Proceedings of the 11th International Workshop on Automation of Software Test
May 2016
105 pages
ISBN:9781450341516
DOI:10.1145/2896921
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

ICSE '16
Sponsor:

Upcoming Conference

ICSE 2025

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2020)STICCER: Fast and Effective Database Test Suite Reduction Through Merging of Similar Test Cases2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)10.1109/ICST46399.2020.00031(220-230)Online publication date: Oct-2020
  • (2019)Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database SchemasIEEE Transactions on Software Engineering10.1109/TSE.2017.278628645:5(427-463)Online publication date: 1-May-2019
  • (2018)A Systematic Review of Cost Reduction Techniques for Mutation Testing: Preliminary Results2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW.2018.00021(1-10)Online publication date: Apr-2018
  • (2018)DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST)10.1109/ICST.2018.00012(12-22)Online publication date: Apr-2018
  • (2016)SchemaAnalyst: Search-Based Test Data Generation for Relational Database Schemas2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2016.93(586-590)Online publication date: Oct-2016
  • (2016)mrstudyr: Retrospectively Studying the Effectiveness of Mutant Reduction Techniques2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2016.92(591-595)Online publication date: Oct-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media