Liaskos, K. and Roper, M. and Wood, M. (2007) Investigating data-flow coverage of classes using evolutionary algorithms. In: GECCO 2007. Assoc Computing Machinery, New York, p. 1140. ISBN 9781595936974
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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