J.B. Kam and J.D. Ullman. Monotone data flow analysis frameworks. Acta Inf., 7(3):305-- 318, 1977.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....favorably with the runtime performances of JDOM, XSLT, or XDuce.#R4: hmm # The analyze operation has no effect at runtime. The cast and get operations perform a linear time DTD validation. III. SUMMARY GRAPHS To obtain static guarantees, we apply the standard dataflow analysis framework [15] [16]. This involves three steps: 1) obtaining an abstract control flow graph for the given program; 2) defining a lattice modeling the abstract data that the analysis manipulates; and 3) describing all kinds of operations in the control flow graph in terms of transfer functions that operate on the ....

J. B. Kam and J. D. Ullman, "Monotone data flow analysis frameworks," Acta Informatica, vol. 7, pp. 305--317, 1977, Springer-Verlag.

.... analysis [4] issues related to interprocedural analysis [5, 46] improved eciency [20, 29] the de nition of data ow problems and their related solutions [6] algorithms for use at the source level [45] complications due to variable aliasing [35] more powerful approaches to data ow analysis [28], general improvements to ow analysis algorithms [8] methods to deal with the huge amounts of information that exhaustive data ow analysis generates [9] application to other areas of computer science including secure information ow [16] and most important to this dissertation, the ....

J.B. Kam and J.D. Ullman. Monotone Data Flow Analysis Frameworks. Acta Informatica, 7(3):305-317, 1977.

....will compare favorably with the runtime performances of JDOM, XSLT, or XDuce. The analyze operation has no effect at runtime. The cast and get operations perform a linear time DTD validation. IV. SUMMARY GRAPHS To obtain static guarantees, we apply the standard dataflow analysis framework [45] [46]. This involves three steps: 1) obtaining an abstract control flow graph for the given program; 2) defining a lattice modeling the abstract data that the analysis manipulates; and 3) describing all kinds of operations in the control flow graph in terms of transfer functions that operate on the ....

J. B. Kam and J. D. Ullman, "Monotone data flow analysis frameworks," Acta Informatica, vol. 7, pp. 305--317, 1977, Springer-Verlag.

....[ can easily be extended to cover finite paths. For every path p = e 1 ; e q ) 2 P[m; n] we define: p ] df Id C if p is the empty path [ e 2 ; e q ) ffi [ e 1 ] otherwise This extension is the key to the so called MOP solution in the sense of Kam and Ullman [23], which can be considered the intuitively desired solution of a DFA problem. The construction of flow graphs is described in [2] a generator for the automatic construction of flow graphs and flow graph systems has recently been introduced in [7] The MOP Approach. The meet over all paths (MOP ....

....it with the data flow facts, a local semantic functional, and a start information of interest, it yields automatically the corresponding concrete algorithm. This generator principle has been realized in the tool kit of [25] Moreover, the Coincidence Theorem of Kildall [24] and Kam and Ullman [23], yields a sufficient condition for the coincidence of the MOP solution and the MFP solution. Theorem 3.2 (Coincidence Theorem) The MFP solution and the MOP solution coincide, i.e. 8 n 2 N: MOP (n) MFP (n) whenever all the semantic functions [ e ] e 2 E, are distributive. 3.2.1 Solving the ....

J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309 -- 317, 1977.

....are used to interpret call edges; we define summaries for all procedures in a program as a fixed point of simultaneous reduction process. 4.3. 1 Data Flow Analysis We frame a simple intraprocedural analysis as solution of a fixed data flow graph, based on a number of published treatments such as [57, 71, 73, 80]. For simplicity, we consider only forward flow problems, and assume that backward flow can be handled by reversing each data flow graph. 4.3.1.1 Data Flow Systems and Solutions A data flow graph G = N, E, no, nf) is a directed graph with node set N, edges E C N x N, and entry and exit nodes ....

J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:305 317, 1977.

....[MW] Note that our analysis is capable of finding the redundancy of array bound checks. Thus, elimination of array bound checks is a side effect of this technique [Gu] Our framework does not fit the mold of existing static analysis formalisms. It is not a monotone data flow analysis framework [KU,He] because the monotone framework requires commutative meet operation on a semilattice and because interpretation of conditional branches is not expressible in the monotone frameworks. The formalism from [We] is also based on commutative operations. Meet of our framework is not commutative. ....

J.B. Kam, J.D. Ullman. Monotone Data Flow Analysis Frameworks. Acta Informatica, v. 7, 1977, 305-317.

....to sophisticated and extensive optimization to approach the full potential of modern computer architectures. Many powerful optimizations rely on static analysis of the source code. Examples of such static analysis include: live variable analysis [7] various forms of constant propagation [10,20], and aliasing analysis [4] All of these static analyses may be realized as instances of flow analysis problems. They share the following general framework [10] 1. A model of the program defines points where information is to be determined, as well as how control may flow between those points ....

....of the source code. Examples of such static analysis include: live variable analysis [7] various forms of constant propagation [10,20] and aliasing analysis [4] All of these static analyses may be realized as instances of flow analysis problems. They share the following general framework [10]: 1. A model of the program defines points where information is to be determined, as well as how control may flow between those points at run time. 2. A set of abstract states representing the desired static information is created. In the case of constant propagation, for example, each abstract ....

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Kam, J. and J. Ullman. "Monotone Data Flow Analysis Frameworks," Acta Informatica 7, 1977, pp. 305-317.

....c( v(o, This data flow framework has been introduced in [3] 4 Solving the Symbolic Evaluation Data Flow Framework Unfortunately the definition of PrpgtCond( prevents the symbolic evaluation framework from being bounded. Thus it cannot be solved by iteration algorithms (compare [9, 7, 8]) Nevertheless we can solve symbolic evaluation frameworks with help of elim ination algorithms (see [11, 10] Note that the set of equations (i 1, n) Ei: xi = Wi(xi, xi, 1) implies a dependency relation on the variables xi. We say that xi on the left side depends on all ....

J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:305-317, 1977.

....steps. The second, more common approach is to perform a program analysis followed by a transformation. Analyses are usually lattice based, and are usually abstract interpretations [CC77,JN95] in the functional community, and dataflow analyses (or more generally, monotone analysis frameworks e.g. KU77] in the imperative community. 2.1 Unreachable code elimination To illustrate the di#erence between pessimism and optimism we consider the problem of unreachable code elimination, that is, removing code that is unreachable in any execution of a program. We review two well known approaches, one ....

J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:305--317, January 1977.

....least solution of Equation System 6 which specifies the consistency between pre conditions of the statements of G expressed in terms of C with respect to some start information c 0 2 C. This annotation is known as the solution of the minimal fixpoint (MFP) approach in the sense of Kam and Ullman [KU77]. Equation System 6. pre(n) c 0 if n = s f [ m ] pre(m) j m 2 predG (n) g otherwise In practice the MFP solution, which we denote by pre c0 , is computed by means of some iterative workset al..gorithm (see Figure 2) We will see that termination and correctness in this approach are a ....

J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309 -- 317, 1977.

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J.B. Kam and J.D. Ullman. Monotone data flow analysis frameworks. Acta Inf., 7(3):305-- 318, 1977.

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Kam, J. B., and Ullman, J. D. Monotone data flow analysis frameworks. Acta Informatica 7 (1977), 305--317.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309--317, 1977.

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John B. Kam and Je#rey D. Ullman. Monotone data flow analysis frameworks. Acta Inf., 7:305--317, 1977.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309--317, 1977.

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John B. Kam and Je#rey D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:305--317, 1977.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309--317, 1977.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Technical Report 169, Department of Electrical Engineering, Princeton University, Princeton, NJ, 1975.

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J. B. Kam and J. D. Ullman, "Monotone data flow analysis frameworks," Acta Informatica, vol. 7, pp. 305--317, 1977, Springer-Verlag.

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Kam J.B., Ullman J.D. Monotone data flow analysis frameworks.--Acta Informatica. -- 1977. -- Vol. 7. -- No 3. -- P. 305--318.

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J. B. KAM AND J. D. ULLMAN, Monotone data flow analysis frameworks, Acta Informatica, 7 (1977), pp. 305--317. Springer-Verlag.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:309--317, 1977.

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J. B. Kam and J. D. Ullman. Monotone data flow analysis frameworks. Acta Informatica, 7:305 -- 317, 1977.

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J. B. Kam and J. D. Ullman, "Monotone data flow analysis frameworks," Acta Informatica, vol. 7, pp. 305--317, 1977, Springer-Verlag.

No context found.

J.B. Kam and J.D. Ullman. Monotone Data Flow Analysis Frameworks. Acta Informatica, 7(3):305-317, 1977.

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