Investigating data-flow coverage of classes using evolutionary algorithms

K. Liaskos; M. Roper; M. Wood

Investigating data-flow coverage of classes using evolutionary algorithms

K. Liaskos, M. Roper, M. Wood

Computer And Information Sciences

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

21 Citations (Scopus)

87 Downloads (Pure)

Abstract

It is not unusual for a software development organization to expend 40% of total project effort on testing, which call be a very laborious and time-consuming process. Therefore, there is a big necessity for test automation. This paper describes an approach to automatically generate test-data for 00 software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow (d-u) coverage. A proof-of-concept tool is presented. The experimental results from testing six Java classes helped us identify three categories of problematic test targets, and suggest that in the future full d-u coverage with a reasonable computational cost may be possible if we overcome these obstacles.

Original language	English
Title of host publication	GECCO 2007
Subtitle of host publication	genetic and evolutionary computation conference proceedings
Place of Publication	New York
Pages	1140-1140
Number of pages	1
Publication status	Published - 2007
Event	Annual Conference of Genetic and Evolutionary Computation Conference - London, United Kingdom Duration: 7 Jul 2007 → 11 Jul 2007

Conference

Conference	Annual Conference of Genetic and Evolutionary Computation Conference
Country/Territory	United Kingdom
City	London
Period	7/07/07 → 11/07/07

Keywords

data-flow coverage
classes
evolutionary algorithms
investigation

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P1140 liaskosFinal published version, 157 KB

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title = "Investigating data-flow coverage of classes using evolutionary algorithms",

abstract = "It is not unusual for a software development organization to expend 40% of total project effort on testing, which call be a very laborious and time-consuming process. Therefore, there is a big necessity for test automation. This paper describes an approach to automatically generate test-data for 00 software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow (d-u) coverage. A proof-of-concept tool is presented. The experimental results from testing six Java classes helped us identify three categories of problematic test targets, and suggest that in the future full d-u coverage with a reasonable computational cost may be possible if we overcome these obstacles. ",

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T1 - Investigating data-flow coverage of classes using evolutionary algorithms

AU - Liaskos, K.

AU - Roper, M.

AU - Wood, M.

PY - 2007

Y1 - 2007

N2 - It is not unusual for a software development organization to expend 40% of total project effort on testing, which call be a very laborious and time-consuming process. Therefore, there is a big necessity for test automation. This paper describes an approach to automatically generate test-data for 00 software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow (d-u) coverage. A proof-of-concept tool is presented. The experimental results from testing six Java classes helped us identify three categories of problematic test targets, and suggest that in the future full d-u coverage with a reasonable computational cost may be possible if we overcome these obstacles.

AB - It is not unusual for a software development organization to expend 40% of total project effort on testing, which call be a very laborious and time-consuming process. Therefore, there is a big necessity for test automation. This paper describes an approach to automatically generate test-data for 00 software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow (d-u) coverage. A proof-of-concept tool is presented. The experimental results from testing six Java classes helped us identify three categories of problematic test targets, and suggest that in the future full d-u coverage with a reasonable computational cost may be possible if we overcome these obstacles.

KW - data-flow coverage

KW - classes

KW - evolutionary algorithms

KW - investigation

UR - http://portal.acm.org/citation.cfm?id=1277183&dl=ACM&coll=ACM

M3 - Conference contribution book

SN - 9781595936974

SP - 1140

EP - 1140

BT - GECCO 2007

CY - New York

T2 - Annual Conference of Genetic and Evolutionary Computation Conference

Y2 - 7 July 2007 through 11 July 2007

ER -

Investigating data-flow coverage of classes using evolutionary algorithms

Abstract

Conference

Keywords

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