Investigating data-flow coverage of classes using evolutionary algorithms

Konstantinos Liaskos; Marc Roper; Murray Wood

doi:10.1145/1276958.1277183

Investigating data-flow coverage of classes using evolutionary algorithms

Konstantinos Liaskos, Marc Roper, Murray Wood

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

21 Citations (Scopus)

Abstract

It is not unusual for a software development organization to expend 40% of total project effort on testing, which can 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 OO 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 '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
Publisher	Association for Computing Machinery
Pages	1140
Number of pages	1
ISBN (Print)	9781595936974
DOIs	https://doi.org/10.1145/1276958.1277183
Publication status	Published - 7 Jul 2007

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10.1145/1276958.1277183

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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 can 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 OO 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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ER -

Investigating data-flow coverage of classes using evolutionary algorithms

Abstract

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