论文信息 - An Input Adjustable Tree Algorithm for Evolutionary Testing

An Input Adjustable Tree Algorithm for Evolutionary Testing

This paper proposes an Input Adjustable Tree Algorithm for the flag problems of evolutionary testing (ET). With the algorithm, the dependencies of input and internal/flag variables can be determined. Based on that, ET guides the search efficiently with the presence of flag variables in the source code.

Carl K. Chang | Hsinyi Jiang | Katsunori Oyama

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