Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy

M. Patrick; R. Alexander; M. Oriol; J.A. Clark

doi:10.1109/APSEC.2013.23

Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy

M. Patrick, R. Alexander, M. Oriol, J.A. Clark

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score.

Original language	English
Title of host publication	Software Engineering Conference (APSEC, 2013 20th Asia-Pacific)
Pages	91-98
Number of pages	8
Volume	1
DOIs	https://doi.org/10.1109/APSEC.2013.23
Publication status	Published - 1 Dec 2013

Bibliographical note

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version: 10.1109/APSEC.2013.23

Keywords

Java
program testing
software fault tolerance
Java programs
fault finding effectiveness
highly efficient subdomain sets
mutation adequacy
mutation score
software testing
test cases
test selection techniques
Covariance matrices
Equations
Fault diagnosis
Optimization
Schedules
Software
Testing
input subdomains
mutation analysis
test case selection
test data generation

Access to Document

10.1109/APSEC.2013.23

patrick_2013c
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version: 10.1109/APSEC.2013.23
Submitted manuscript, 443 KB

Cite this

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title = "Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy",

abstract = "Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score.",

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AB - Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score.

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KW - test cases

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