This paper shows howalarge class of interprocedural dataflow-analysis problems can be solved precisely in polynomial time. The only restrictions are that the set of dataflowfacts is a finite set, and that the dataflowfunctions distribute overthe confluence operator (either union or intersection). This class of problems includes---but is not limited to---the classical separable problems (also known as "gen/kill" or "bit-vector" problems)---e.g.,reaching definitions, available expressions, and live variables. In addition, the class of problems that our techniques handle includes manynon-separable problems, including trulylive variables, copyconstant propagation, and possibly-uninitialized variables. Anovelaspect of our approach is that an interprocedural dataflow-analysis problem is transformed into a special kind of graph-reachability problem (reachability along interprocedurally realizable paths). The paper presents three polynomial-time algorithms for the realizable-path reachability problem: an exhaustive version, a second exhaustive version that may be more appropriate in the incremental and/or interactive context, and a demand version. The first and third of these algorithms are asymptotically faster than the best previously known realizable-path reachability algorithm. An additional benefit of our techniques is that theylead to improvedalgorithms for twoother kinds of interproceduralanalysis problems: interprocedural flow-sensitive side-effect problems (as studied by Callahan) and interprocedural program slicing (as studied by Horwitz, Reps, and Binkley). CR Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors - compilers, optimization;E.1 [Data

We present an interprocedural generalization of the well-known (intraprocedural) Coincidence Theorem of Kam and Ullman, which provides a sufficient condition for the equivalence of the meet over all paths (MOP ) solution and the maximal fixed point (MFP ) solution to a data flow analysis problem. This generalization covers arbitrary imperative programs with recursive procedures, global and local variables, and formal value parameters. In the absence of procedures, it reduces to the classical intraprocedural version. In particular, our stack-based approach generalizes the coincidence theorems of Barth and Sharir/Pnueli for the same setup, which do not properly deal with local variables of recursive procedures. 1 Motivation Data flow analysis is a classical method for the static analysis of programs that supports the generation of efficient object code by "optimizing" compilers (cf. [He, MJ]). For imperative languages, it provides information about the program states that may occur at s...

Abstract. Constant propagation aims at identifying expressions that always yield a unique constant value at run-time. It is well-known that constant propagation is undecidable for programs working on integers even if guards are ignored as in non-deterministic flow graphs. We show that polynomial constants are decidable in non-deterministic flow graphs. In polynomial constant propagation, assignment statements that use the operators +, −, ∗ are interpreted exactly but all assignments that use other operators are conservatively interpreted as non-deterministic assignments. We present a generic algorithm for constant propagation via a symbolic weakest precondition computation and show how this generic algorithm can be instantiated for polynomial constant propagation by exploiting techniques from computable ring theory. 1

this paper we consider two elaborations of this algorithm, which are dealt with in Part I and Part II, respectively. Part I deals with the problem that the full variant of the algorithm of [SKR1] may excessively introduce trivial redefinitions of registers in order to cover a single computation. Rosen, Wegman and Zadeck avoided such a too excessive introduction of trivial redefinitions by means of some practically oriented restrictions, and they proposed an efficient algorithm, which optimally moves the computations of acyclic flow graphs under these additional constraints (the algorithm is "RWZoptimal " for acyclic flow graphs) [RWZ]. Here we adapt our algorithm to this notion of optimality. The result is a modular and efficient algorithm, which avoids a too excessive introduction of trivial redefinitions along the lines of [RWZ], and is RWZ-optimal for arbitrary flow graphs. Part II modularly extends the algorithm of [SKR1] in order to additionally cover strength reduction. This extension generalizes and improves all classical techniques for strength reduction in that it overcomes their structural restrictions concerning admissible program structures (e.g. previously determined loops) and admissible term structures (e.g. terms built of induction variables and region constants). Additionally, the program transformation obtained by our algorithm is guaranteed to be safe and to improve run-time efficiency. Both properties are not guaranteed by previous techniques. Structure of the Paper