A Multi-Objective Approach To Search-Based Test Data Generation

Mark Harman; Kiran Lakhotia; Phil McMinn

A Multi-Objective Approach To Search-Based Test Data Generation

Mark Harman, Kiran Lakhotia, Phil McMinn

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

124 Citations (Scopus)

Abstract

There has been a considerable body of work on search-based test; data, generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic: testers will want, to find test; sets that meet. several objectives simultaneously in order to maximize the value obtained front the inherently expensive process of running the test, cases and examining the output they produce. This paper introduces multi-objective branch coverage. The paper presents results front a case study of the twin objectives of branch coverage, and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi objective evolutionary algorithms are Suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.

Original language	English
Title of host publication	Unknown
Place of Publication	NEW YORK
Publisher	ASSOC COMPUTING MACHINERY
Pages	1098 - 1105
Number of pages	8
ISBN (Print)	978-1-59593-697-4
Publication status	Published - 2007
Event	Annual Conference of Genetic and Evolutionary Computation Conference - London, ENGLAND Duration: 7 Jul 2007 → 11 Jul 2007

Publication series

Name	GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2

Conference

Conference	Annual Conference of Genetic and Evolutionary Computation Conference
City	London, ENGLAND
Period	7/07/2007 → 11/07/2007

Cite this

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title = "A Multi-Objective Approach To Search-Based Test Data Generation",

abstract = "There has been a considerable body of work on search-based test; data, generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic: testers will want, to find test; sets that meet. several objectives simultaneously in order to maximize the value obtained front the inherently expensive process of running the test, cases and examining the output they produce. This paper introduces multi-objective branch coverage. The paper presents results front a case study of the twin objectives of branch coverage, and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi objective evolutionary algorithms are Suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.",

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publisher = "ASSOC COMPUTING MACHINERY",

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note = "Annual Conference of Genetic and Evolutionary Computation Conference ; Conference date: 07-07-2007 Through 11-07-2007",

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TY - CHAP

T1 - A Multi-Objective Approach To Search-Based Test Data Generation

AU - Harman, Mark

AU - Lakhotia, Kiran

AU - McMinn, Phil

PY - 2007

Y1 - 2007

N2 - There has been a considerable body of work on search-based test; data, generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic: testers will want, to find test; sets that meet. several objectives simultaneously in order to maximize the value obtained front the inherently expensive process of running the test, cases and examining the output they produce. This paper introduces multi-objective branch coverage. The paper presents results front a case study of the twin objectives of branch coverage, and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi objective evolutionary algorithms are Suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.

AB - There has been a considerable body of work on search-based test; data, generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic: testers will want, to find test; sets that meet. several objectives simultaneously in order to maximize the value obtained front the inherently expensive process of running the test, cases and examining the output they produce. This paper introduces multi-objective branch coverage. The paper presents results front a case study of the twin objectives of branch coverage, and dynamic memory consumption for both real and synthetic programs. Several multi-objective evolutionary algorithms are applied. The results show that multi objective evolutionary algorithms are Suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade-offs between the two simultaneous objectives.

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M3 - Conference paper

SN - 978-1-59593-697-4

T3 - GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2

SP - 1098

EP - 1105

BT - Unknown

PB - ASSOC COMPUTING MACHINERY

CY - NEW YORK

T2 - Annual Conference of Genetic and Evolutionary Computation Conference

Y2 - 7 July 2007 through 11 July 2007

ER -

A Multi-Objective Approach To Search-Based Test Data Generation

Abstract

Publication series

Conference

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