Ryder, B.G. and Paull, M.C., "Incremental data flow analysis algorithms," ACM Trans. Program. Lang. Syst. 10(1) pp. 1-50 (January 1988).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....results used in computing the output f(x) also need to be maintained for efficient incremental computation of f(x Phi y) Moreover, certain auxiliary information about x may need to be discovered and maintained as well. Numerous techniques for incremental computation have been developed, e.g. [3, 4, 16, 20, 21, 24, 31, 36, 38, 40, 43, 44, 47, 52]. In [31] we give a systematic transformational approach for deriving an incremental program f from a given program f and an input change Phi. The basic idea is to identify in the computation of f(x Phi y) those subcomputations that are also performed in the computation of f(x) and whose ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....statements on program behavior. Data dependence information captures the effects of data interactions on program behavior. Tools such as program slicers use control and data dependence information for tasks such as debugging, impact analysis, and regression testing. Much research (e.g. [3, 6, 8, 21, 24, 27]) has addressed the problems of computing and utilizing intraprocedural dependences: dependences within procedures that can be computed by analyzing procedures independently. That research has considered both control and data dependence. 1 To function effectively on whole programs, however, ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....statements on program behavior. Data dependence information captures the effects of data interactions on program behavior. Tools such as program slicers use control and data dependence information for tasks such as debugging, impact analysis, and regression testing. Much research (e.g. [3, 6, 8, 21, 24, 27]) has addressed the problems of computing and utilizing intraprocedural dependences: dependences within procedures that can be computed by analyzing procedures independently. That research has considered both control and data dependence. An earlier version of this paper, Computation of ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....extended by Podgurski and Clarke who distinguished between several notions of control dependence [25] Bilardi and Pingali [3] proposed a generalized framework to unify many different such notions. Most of the research in this area has focused on computing intraprocedural control dependence [8, 24, 6, 26, 27]. In this approach, each procedure is treated in isolation, by ignoring transfer of control due to procedure calls and returns. While adequate for some applications such as instruction scheduling, ignoring calls and returns is not an option for other applications such as interprocedural dataflow ....

B. G. Ryder and M. C. Paull. Incremental data-flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....performance. 2.4.2 Incremental Analysis Maintaining information about the results of previous analyses and optimizations, and reusing that information for subsequent compilations is another technique to manage the potential for compile time and space increases. The method of incremental analysis [57, 58, 65, 67, 68, 75] allows the compiler to analyze only the parts of the program that have been modified or added since the previous compilation. The benefit in terms of scalability of incremental analysis comes from reducing the amount of recompilation, reanalysis and reoptimization necessary; however, there is ....

B. G. Ryder and M. C. Paull. Incremental data-flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, Jan. 1988.

....consists of incremental algorithms, which includes dynamic algorithms and on line algorithms. These are particular algorithms manually derived to handle particular problems and particular input changes. Examples are incremental parsing [40] attribute evaluation [73, 85] data flow analysis [76], circuit evaluation [6] constraint solving [30] transitive closure [86] shortest path [70] minimum spanning tree [29, 23] connectivity [23, 71] and scheduling. Since these algorithms are manually derived to solve particular incremental problems, we say that they are ad hoc. The second ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Trans. Program. Lang. Syst., 10(1):1--50, Jan. 1988.

....the found aliases [SH97a, ZRL96, ZRL98, YRL98] 3. 3 Incremental Data Flow Analyses Many incremental algorithms have been developed for data flow analysis, which are useful, especially in a programming environment [Zad84] Some incremental analyses use incremental elimination methods [Bur90, CR88, RP88] some are based on the technique of restarting iteration [CK84, PS89, YRL99] and some are a combination of these two techniques [MR90] An incremental elimination method first partitions the flow graph, and then establishes the data flow function from the head node of a region to each interior ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....from one another, making use of the old output in computing a new output rather than computing from scratch. The large number of works on incremental computation in recent years and their many applications [RR93] demonstrated by various incremental algorithms such as [GM79,JG82,RTD83,Yeh83,RP88,Van88,FMB90,AHR 90,RR94] and general incremental computation approaches such as [Ear76,Pai81,PK82,Pai84,HT86, CP89,PT89,FT90,Smi90,Smi91,YS91,SH91,Sun91,Hoo92,van92,Fie93] motivated us to look for the fundamentals of incremental computation and their role in efficient computation. The goal ....

....algorithm Our first class includes particular incremental algorithms designed for particular problems dealing with particular input changes. Examples include incremental parsing [GM79,JG82] incremental attribute evaluation [RTD83,Yeh83,YK88,LMOW88, Jon90] incremental data flow analysis [Zad84,RP88,Bur90,MR90,RR94] incremental circuit evaluation [AHR 90] and incremental constraint solving [Van88,FMB90] The study of dynamic graph algorithms, such as transitive closure algorithms [Yel93] can be viewed as falling into this class. These incremental algorithms are called explicit ....

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B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....need to incrementally update the control flow, control dependence, SSA, and call graphs, as well as recalculate live range, constant, symbolic, interprocedural, and dependence testing information. Several algorithms for incremental analysis can be found in the literature; e.g. dataflow analysis [47, 58], interprocedural analysis [10, 46] interprocedural recompilation analysis [11] as well as dependence analysis [45] However, few of these algorithms have been implemented and evaluated in an interactive environment. Rather than tackle all these problems at once, we chose a simple yet practical ....

B. Ryder and M. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....The problem of avoiding the costly re computation of exhaustive data flow solutions is not unique to integration testing. It arises in virtually all data flow applications that deal with evolving software. Previously, incremental data flow algorithms have been proposed to address this problem [16, 13]. IncremenCopyright 1996 IEEE. Published in the Proceedings of the 18th International Conference on Software Engineering (ICSE 18) March 25 29, 1996, Berlin, Germany. Personal use of this material is permitted. However, permission to reprint republish this material for advertising or promotional ....

....determined as part of an interprocedural program slice [21, 9] However, previous precise interprocedural algorithms [9] are not suitable for computing interprocedural def use pairs since their computation is not explicit but interleaved with the slice construction. Incremental data flow analysis [16, 13] addresses the problem of updating an existing exhaustive solution in response to program changes. Thus, in contrast to demand driven analysis, incremental analysis requires the computation and maintenance of an exhaustive solution. As pointed out earlier, the incremental update problem that ....

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B.G. Ryder and M.C. Paull. Incremental data flow analysis algorithms. ACM Trans. Programming Languages and Systems, 10(1):1--50, `88.

....as a slicer, which uses control and data dependencies to identify a superset of the statements that could semantically affect another statement, can omit semantic dependencies if it utilizes inappropriate definitions or computations of data or control dependence information. Much research (e.g. [4, 6, 8, 17, 19, 21]) has addressed the problem of computing intraprocedural dependencies: dependencies that exist within procedures and can be computed by analyzing procedures independently. To function effectively on whole programs, however, tools that require dependence information also require information about ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Trans. on Prog. Lang. and Sys., 10(1):1--50, Jan. 1988.

....results used in computing the output f(x) also need to be maintained for efficient incremental computation of f(x Phi y) Moreover, certain auxiliary information about x may need to be discovered and maintained as well. Numerous techniques for incremental computation have been developed, e.g. [3, 4, 16, 20, 21, 24, 31, 36, 38, 40, 43, 44, 47, 52]. In [31] we give a systematic transformational approach for deriving an incremental program f 0 from a given program f and an input change Phi. The basic idea is to identify in the computation of f(x Phi y) those subcomputations that are also performed in the computation of f(x) and whose ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....test case generation, regression testing, dynamic execution profiling, and static and dynamic slicing (e.g. 3, 6, 12, 13, 20] require controlflow information. Much research has addressed the problems of computing such analysis information for individual procedures (intraprocedural) e.g. [5, 21]) and for interacting procedures (interprocedural) e.g. 15] Some of this research has addressed the problems of performing analyses for programs with transfers of control, such as continue and goto statements, that can affect the analyses at the intraprocedural level (e.g. 1] Other ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Trans. on Prog. Lang. and Sys., 10(1):1--50, Jan. 1988.

....the next node reached. Note that these definitions specify only executable definition use associations. With imperative programs, we can conservatively identify executable definition use associations by using dataflow analysis algorithms that propagate definitions along control flow paths (e.g. [1, 12, 22, 26]) This approach does not directly apply to form based programs, because in form based programs the flow of control associated with evaluation orders of cells is not explicit, and varies over different evaluation engines. However, in non recursive form based programs such as those we are ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, Jan. 1988.

....single assignment graph (ssa) CFR 89] and call graphs, as well as recalculate scalar live range, constant, symbolic, interprocedural, and dependence testing information. Several algorithms for performing incremental analysis are found in the literature; for example, data flow analysis [RP88, Zad84] interprocedural analysis [Bur90, RC86] interprocedural recompilation analysis [BCKT90] as well as dependence analysis [Ros90] However, few of these algorithms have been implemented and evaluated in an interactive environment. Rather than tackle all these problems at once, we chose a ....

B. Ryder and M. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

.... compiler optimization [ASU86] practical dependence analysis in programs with procedure calls [Ban88, Pol88, Wol89] data flow based testing [HS91, BH93, HFGO94, Ost90, Wey94, CR99, FW93, FI98] incremental semantic change analysis of software [Bur90, BR90, CR88, CK84, MR90a, MR91, PS89, Ryd83, RP88, YRLS97, YRL99] interprocedural def use relations [PLR94, HS94, GH98, CR99, Cha99] and e#ective static interprocedural program slicing [GL91, HRB90, OO84, RR95, GS96, LH96, HC98, SHR99, TCFR96, Tip96, Ven91, Wei84, TAFM97, AG96, AG98] Many of these key applications in parallel and sequential ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

.... the same solution from the incremental and exhaustive algorithms) Related Work Many incremental algorithms have been developed for data flow analysis, which are useful, especially in a programming environment [Zad84] Some incremental analyses use incremental elimination methods [Bur90, CR88, RP88] some are based on the technique of restarting iteration [CK84, PS89] and some are a combination of these two techniques [MR90] When a change is made, restarting iteration is a good mechanism for reusing the previously computed information. However, a precise solution for the updated problem ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....of data usage in the program. Compile time analysis provides potentially useful information which can be utilized to insure the safety of program optimizations and transformations. Analysis information is also essential for data flow based testing [10, 12, 13, 18, 29, 31] semantic change analysis [5, 28, 33, 34, 35, 42, 43], integration of program versions [16, 17, 41] and various other optimizations, such as run time check elision [1] e.g. verifying that variables are initialized before they are used) In languages with general purpose pointer usage, two names may access the same location during execution; they ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....that incremental analysis will be substantially cheaper than total reanalysis. Related Work Many incremental algorithms have been developed for the data flow analysis, which are useful, especially in a programming environment [Zad84] Some incremental analyses use incremental elimination methods [Bur90, CR88, RP88]; some are based on the technique of restarting iteration [CK84, PS89] When a change is made, restarting iteration from the previously computed solution does not always yield a precise solution. RMP88] shows some su#cient conditions under which restarting iteration results in the same solution ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

.... 1 For other pointer aliasing algorithms, see [YRL98] Related Work Many incremental algorithms have been developed for the data flow analysis, which are useful, especially in a programming environment [Zad84] Some incremental analyses use incremental elimination methods [Bur90, CR88, RP88] some are based on the technique of restarting iteration [CK84, PS89] and some are a combination of these two techniques [MR90] When a change is made, restarting iteration is a good mechanism for reusing the previously computed information. However, a precise solution for the updated problem ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

....is costly. Instead of re computing the data flow information from scratch, incremental analysis techniques seek to update the information based on the former solution and knowledge of the source changes. Two methods are commonly used in incremental analysis: iteration [BR90, PS89] and elimination [RP88]. Combining these two approaches, Marlowe and Ryder [MR90, MR91] proposed a hybrid algorithm for incremental analysis. Intuitively, the hybrid algorithm partitions the flow graph into components, then uses iterative techniques within components and applies the idea of elimination to information ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, vol. 10, no. 1, pages 1-50, January 1988.

....new and extensive empirical results with two of the algorithms. The first MODC algorithm in the schema was discussed in [LRZ93] Ban88, Pol88, Wol89] data flow based testing [HS91, BH93, Ost90, Wey94] incremental semantic change analysis of software [Bur90, BR90, CK84, MR90a, PS89, Ryd83, RP88] interprocedural def use relations [PLR94, HS94, GH98] and effective static interprocedural program slicing [GL91, HRB90, OO84, RR95, GS96, LH96, TCFR96, Tip96, Ven91, Wei84, TAFM97, AG96] Many of these key applications in parallel and sequential programming environments need interprocedural ....

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, January 1988.

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Ryder, B.G. and Paull, M.C., "Incremental data flow analysis algorithms," ACM Trans. Program. Lang. Syst. 10(1) pp. 1-50 (January 1988).

No context found.

B. G. Ryder and M. C. Paull. Incremental data flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1--50, 1988.

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B. G. Ryder and M. C. Paull. Incremental data-flow analysis algorithms. ACM Transactions on Programming Languages and Systems, 10(1):1-- 50, January 1988.

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