Incremental data-flow analysis algorithms
ACM Transactions on Programming Languages and Systems (TOPLAS)
The program summary graph and flow-sensitive interprocedual data flow analysis
PLDI '88 Proceedings of the ACM SIGPLAN 1988 conference on Programming Language design and Implementation
Interprocedural side-effect analysis in linear time
PLDI '88 Proceedings of the ACM SIGPLAN 1988 conference on Programming Language design and Implementation
Fast interprocedual alias analysis
POPL '89 Proceedings of the 16th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
An Incremental Version of Iterative Data Flow Analysis
IEEE Transactions on Software Engineering
Interprocedural slicing using dependence graphs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Analyzing aliases of reference formal parameters
POPL '85 Proceedings of the 12th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A practical interprocedural data flow analysis algorithm
Communications of the ACM
Efficient computation of flow insensitive interprocedural summary information
SIGPLAN '84 Proceedings of the 1984 SIGPLAN symposium on Compiler construction
Flow Analysis of Computer Programs
Flow Analysis of Computer Programs
A precise inter-procedural data flow algorithm
POPL '81 Proceedings of the 8th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
An efficient way to find the side effects of procedure calls and the aliases of variables
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Selecting and Using Data for Integration Testing
IEEE Software
The Precise Determination of Definition-Use Pairs in the Interprocedural Case
Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1 - Volume I
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Compiler optimization, parallel processing, data flow testing, and symbolic debugging can benefit from interprocedural data flow analysis. However, the live, reaching definition, and most summary data flow problems are theoretically intractable in the interprocedural case. A method is presented that reduces the exponential time bound with the help of an algorithm that solves the problem in polynomial time. Either the resulting sets contain precise results or the missing (or additional) results do not cause any problems during their use. We also introduce the double iterative framework, where one procedure is processed at a time. The results of the intraprocedural analysis of procedures then propagates along the edges of the call multi-graph. In this way the intra and interprocedural analyses are executed alternately until there is no change in any result set. This method can be applied to any known interprocedural data flow problem. Here the algorithms for the kill, live variables, and reaching definitions problems are presented. Besides for precision, the algorithms can be used for very large programs, and since inter and intraprocedural analyses can be optimized separately, the method is fast as well.