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We present a method for optimizing software testing efficiency by identifying the most error prone path clusters in a program. We do this by developing variable length genetic algorithms that optimize and select the software path clusters which are weighted with sources of error indexes. Although various methods have been applied to detecting and reducing(More)
This paper is the continuation of previously published work in which we have been analysing different methods – traditionally used in speech recognition – for their suitability to be applied to Environmental Sound Recognition. While current research devotes much effort to speech and speaker recognition, Environmental Sound Recognition is an area where(More)
In earlier work we have demonstrated that GA can successfully identify error prone paths that have been weighted according to our weighting scheme. In this paper we investigate whether the depth of strata in the software affects the performance of the GA. Our experiments show that the GA performance changes throughout the paths. It performs better in the(More)