Bebop
Thomas Ball, Sriram K. Rajamani
Abstract
Thomas Ball, Sriram K. Rajamani
Abstract
Flow-sensitive data analyses can lose precision because they assume that all paths in a control-flow graph are executable (feasible). Path-sensitive dataflow analyses can rule out infeasible paths by tracking correlations between dataflow facts. To track such correlations, in general, requires recording a set of sets of facts per statement in a program. Naive representation of such sets can lead to a very high memory consumption and running time.
OpenAlex reports 81 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Flow-sensitive data analyses can lose precision because they assume that all paths in a control-flow graph are executable (feasible). Path-sensitive dataflow analyses can rule out infeasible paths by tracking correlations between dataflow facts. To track such correlations, in general, requires recording a set of sets of facts per statement in a program. Naive representation of such sets can lead to a very high memory consumption and running time.
Key concepts: Dataflow, Computer science, Executable, Control flow graph, Data-flow analysis, Data flow diagram, Control flow, Graph