Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
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We present a comprehensive approach to performing data flow analysis in parallel. We first identify three types of parallelism inherent in the data flow solution process: independent-problem parallelism, separate-unit parallelism and algorithmic parallelism. We then describe a unified framework to exploit them. Our investigations of typical Fortran programs reveal an abundance of the last two types of parallelism. In particular, we illustrate the exploitation of algorithmic parallelism in the design of our parallel hybrid data flow analysis algorithm and report on its empirical performance.