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72
Solving ShapeAnalysis Problems in Languages with Destructive Updating
 POPL '96
, 1996
"... This paper concerns the static analysis of programs that perform destructive updating on heapallocated storage. We give an algorithm that conservatively solves this problem by using a finite shapegraph to approximate the possible “shapes” that heapallocated structures in a program can take on. In ..."
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Cited by 301 (20 self)
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This paper concerns the static analysis of programs that perform destructive updating on heapallocated storage. We give an algorithm that conservatively solves this problem by using a finite shapegraph to approximate the possible “shapes” that heapallocated structures in a program can take on. In contrast with previous work, our method M even accurate for certain programs that update cyclic data structures. For example, our method can determine that when the input to a program that searches a list and splices in a new element is a possibly circular list, the output is a possibly circular list.
Flow analysis and optimization of LISPlike structures
 In Conference Record of the Fourth ACM Symposium on Principles of Programming Languages
, 1979
"... In [12] the authors introduced ..."
Partial Online Cycle Elimination in Inclusion Constraint Graphs
 IN PROCEEDINGS OF THE 1998 ACM SIGPLAN CONFERENCE ON PROGRAMMING LANGUAGE DESIGN AND IMPLEMENTATION
, 1998
"... Many program analyses are naturally formulated and implemented using inclusion constraints. We present new results on the scalable implementation of such analyses based on two insights: first, that online elimination of cyclic constraints yields ordersofmagnitude improvements in analysis time for ..."
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Cited by 123 (13 self)
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Many program analyses are naturally formulated and implemented using inclusion constraints. We present new results on the scalable implementation of such analyses based on two insights: first, that online elimination of cyclic constraints yields ordersofmagnitude improvements in analysis time for large problems; second, that the choice of constraint representation affects the quality and efficiency of online cycle elimination. We present an analytical model that explains our design choices and show that the model's predictions match well with results from a substantial experiment.
A Finite Presentation Theorem for Approximating Logic Programs
 In Seventeenth Annual ACM Symposium on Principles of Programming Languages
, 1990
"... In program analysis, a key notion used to approximate the meaning of a program is that of ignoring intervariable dependencies. We formalize this notion in logic programming in order to define an approximation to the meaning of a program. The main result proves that this approximation is not only re ..."
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Cited by 97 (15 self)
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In program analysis, a key notion used to approximate the meaning of a program is that of ignoring intervariable dependencies. We formalize this notion in logic programming in order to define an approximation to the meaning of a program. The main result proves that this approximation is not only recursive, but that it can be finitely represented in the form of a cyclic term graph. This explicit representation can be used as a starting point for logic program analyzers. A preliminary version appears in the Proceedings, 17 th ACM Symposium on POPL. y School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 152133890 z IBM Thomas J. Watson Research Center, PO Box 218, Yorktown Heights, NY 10598 Section 1: Introduction 1 1 Introduction The problem at hand is: given a logic program, obtain an approximation of its meaning, that is, obtain an approximation of its least model. The definition of the approximation should be declarative (so that results can be proved ab...
Automatic Complexity Analysis
 In Functional Programming Languages and Computer Architecture, in FPCA ’89 Conference Proceedings, 144– 156, ACM
, 1989
"... One way to analyse programs is to to derive expressions for their computational behaviour. A time bound function (or worstcase complexity) gives an upper bound for the computation time as a function of the size of input. We describe a system to derive such time bounds automatically using abstract ..."
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Cited by 80 (2 self)
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One way to analyse programs is to to derive expressions for their computational behaviour. A time bound function (or worstcase complexity) gives an upper bound for the computation time as a function of the size of input. We describe a system to derive such time bounds automatically using abstract interpretation. The semanticsbased setting makes it possible to prove the correctness of the time bound function. The system can analyse programs in a firstorder subset of Lisp and we show how the system also can be used to analyse programs in other languages.
Formal Language, Grammar and SetConstraintBased Program Analysis by Abstract Interpretation
, 1995
"... Grammarbased program analysis à la Jones and Muchnick and setconstraintbased program analysis à la Aiken and Heintze are static analysis techniques that have traditionally been seen as quite different from abstractinterpretationbased analyses, in particular because of their apparent noniterati ..."
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Cited by 74 (10 self)
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Grammarbased program analysis à la Jones and Muchnick and setconstraintbased program analysis à la Aiken and Heintze are static analysis techniques that have traditionally been seen as quite different from abstractinterpretationbased analyses, in particular because of their apparent noniterative nature. For example, on page 18 of N. Heintze thesis, it is alleged that ``The finitary nature of abstract interpretation implies that there is a fundamental limitation on the accuracy of this approach to program analysis. There are decidable kinds of analysis that cannot be computed using abstract interpretation (even with widening and narrowing). The setbased analysis considered in this thesis is one example''. On the contrary, we show that grammar and setconstraintbased program analyses are similar abstract interpretations with iterative fixpoint computation using either a widening or a finitary grammar/setconstraints transformer or even a finite domain for each particular program. The understanding of grammarbased and setconstraintbased program analysis as a particular instance of abstract interpretation of a semantics has several advantages. First, the approximation process is formalized and not only explained using examples. Second, a domain of abstract properties is exhibited which is of general scope. Third, these analyses can be easily combined with other abstractinterpretationbased analyses, in particular for the analysis of numerical values. Fourth, they can be generalized to very powerful attributedependent and contextdependent analyses. Finally, a few misunderstandings may be removed.
Set Constraints are the Monadic Class
, 1992
"... We investigate the relationship between set constraints and the monadic class of firstorder formulas and show that set constraints are essentially equivalent to the monadic class. From this equivalence we can infer that the satisfiability problem for set constraints is complete for NEXPTIME. Mor ..."
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Cited by 72 (0 self)
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We investigate the relationship between set constraints and the monadic class of firstorder formulas and show that set constraints are essentially equivalent to the monadic class. From this equivalence we can infer that the satisfiability problem for set constraints is complete for NEXPTIME. More precisely, we prove that this problem has a lower bound of NTIME(c n= log n ). The relationship between set constraints and the monadic class also gives us decidability and complexity results for certain practically useful extensions of set constraints, in particular "negative projections" and subterm equality tests.
Set Constraints: Results, Applications and Future Directions
 In Second Workshop on the Principles and Practice of Constraint Programming
"... . Set constraints are a natural formalism for many problems that arise in program analysis. This paper provides a brief introduction to set constraints: what set constraints are, why they are interesting, the current state of the art, open problems, applications and implementations. 1 Introduction ..."
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Cited by 71 (4 self)
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. Set constraints are a natural formalism for many problems that arise in program analysis. This paper provides a brief introduction to set constraints: what set constraints are, why they are interesting, the current state of the art, open problems, applications and implementations. 1 Introduction Set constraints are a natural formalism for describing relationships between sets of terms of a free algebra. A set constraint has the form X ` Y , where X and Y are set expressions. Examples of set expressions are 0 (the empty set), ff (a setvalued variable), c(X; Y ) (a constructor application), and the union, intersection, or complement of set expressions. Recently, there has been a great deal of interest in program analysis algorithms based on solving systems of set constraints, including analyses for functional languages [AWL94, Hei94, AW93, AM91, JM79, MR85, Rey69], logic programming languages [AL94, HJ92, HJ90b, Mis84], and imperative languages [HJ91]. In these algorithms, sets of...
The complexity of set constraints
, 1993
"... Set constraints are relations between sets of terms. They have been used extensively in various applications in program analysis and type inference. We present several results on the computational complexity of solving systems of set constraints. The systems we study form a natural complexity hierar ..."
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Cited by 67 (11 self)
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Set constraints are relations between sets of terms. They have been used extensively in various applications in program analysis and type inference. We present several results on the computational complexity of solving systems of set constraints. The systems we study form a natural complexity hierarchy depending on the form of the constraint language.
Program specialization via program slicing
 Proceedings of the Dagstuhl Seminar on Partial Evaluation, volume 1110 of Lecture Notes in Computer Science
, 1996
"... This paper concerns the use of program slicing to perform a certain kind of programspecialization operation. The specialization operation that slicing performs is different from the specialization operations performed by algorithms for partial evaluation, supercompilation, bifurcation, and deforest ..."
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Cited by 55 (4 self)
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This paper concerns the use of program slicing to perform a certain kind of programspecialization operation. The specialization operation that slicing performs is different from the specialization operations performed by algorithms for partial evaluation, supercompilation, bifurcation, and deforestation. In particular, we present an example in which the specialized program that we create via slicing could not be created as the result of applying partial evaluation, supercompilation, bifurcation, or deforestation to the original unspecialized program. Specialization via slicing also possesses an interesting property that partial evaluation, supercompilation, and bifurcation do not possess: The latter operations are somewhat limited in the sense that they support tailoring of existing software only according to the ways in which parameters of functions and procedures are used in a program. Because parameters to functions and procedures represent the range of usage patterns that the designer of a piece of software has anticipated, partial evaluation, supercompilation, and bifurcation support specialization only in ways that have already been “foreseen ” by the software’s author. In contrast, the specialization operation that slicing supports permits programs to be specialized in ways