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The control flow graph (CFG) representation of a procedure used by virtuallyall flow-sensitive program analyses, admits a large number of infeasiblecontrol flow paths i.e., these paths do not occur in any execution of theprogram. Hence the information reaching along infeasible paths in an analysisis spurious. This affects the precision of the conventional maximum fixed point(MFP) solution of the data flow analysis, because it includes the informationreaching along all control flow paths. The existing approaches for removingthis imprecision are either specific to a data flow problem with nostraightforward generalization or involve control flow graph restructuringwhich may exponentially blow up the size of the CFG.  We lift the notion of MFP solution to define the notion of feasible path MFP(FPMFP) solutions that exclude the data flowing along known infeasible paths.The notion of FPMFP is generic and does not involve CFG restructuring. Instead,it takes externally supplied information about infeasible paths and lifts anydata flow analysis to an analysis that maintains the distinctions betweendifferent paths where these distinctions are beneficial, and ignores them wherethey are not. Thus it gets the benefit of a path-sensitive analysis where it isuseful without performing a conventional path-sensitive analysis.  We evaluated the proposed feasible path MFP solutions for reachingdefinitions analysis and potentially uninitialized variable analysis on 30benchmarks. The evaluation results indicate that precision improvement in thesetwo analyses respectively reduce the number def-use pairs by up to 13.6%(average 2.87%, geometric mean 1.75%), and reduce the potentially uninitializedvariable alarms by up to 100% (average 18.5%, geo. mean 3%). We found that theFPMFP computation time was 2.9X of MFP computation time on average.

Computing Maximum Fixed Point Solutions over Feasible Paths in Data Flow Analyses