Derivation of Deterministic Inverse Programs Based on LR Parsing

Glück, Robert; Kawabe, Masahiko

doi:10.1007/978-3-540-24754-8_21

Cited by 29 publications

(28 citation statements)

References 11 publications

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“…Since now an accumulative function can be viewed as non-accumulative context-generating functions, we hope that we can extend usual range-analysis-based program-inversion methods [16,19,24] to those functions, and hope that a program-inversion method developed in this way would be a good alternative to the existing approaches [15,26,27]. Another future direction is to develop an inverse computation method that can handle more kinds of copying.…”

Section: Resultsmentioning

confidence: 99%

“…Glück and Kawabe [15] uses the LR-parsing technique. In their system, if the grammar obtained from a program is LR-parsable, inverse program based on LR-parsing is derived.…”

Section: Related Work 61 Inverse Computationmentioning

confidence: 99%

“…Some of the existing methods [1,16,24] do not terminate for functions with accumulations. Some approaches [15,26,27] can handle certain accumulative computations efficiently, but they do not work for non-injective functions such as eval . Although some inverse computation methods terminate for accumulative functions [13,20], the complexity upper bound is unclear when there are also multiple data traversals.…”

Section: Introductionmentioning

confidence: 99%

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Polynomial-time inverse computation for accumulative functions with multiple data traversals

Matsuda

Inaba

Nakano

2012

Higher-Order Symb Comput

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Inverse computation has many applications such as serialization/deserialization, providing support for undo, and test-case generation for software testing. In this paper, we propose an inverse computation method that always terminates for a class of functions known as parameter-linear macro tree transducers, which involve multiple data traversals and the use of accumulations. The key to our method is the observation that a function in the class can be regarded as a non-accumulative context-generating transformation without multiple data traversals. Accordingly, we demonstrate that it is easy to achieve terminating inverse computation for the class by context-wise memoization of the inverse computation results. We also show that when we use a tree automaton to express the inverse computation results, the inverse computation runs in time polynomial to the size of the original output and the textual program size.

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Section: Resultsmentioning

confidence: 99%

“…Glück and Kawabe [15] uses the LR-parsing technique. In their system, if the grammar obtained from a program is LR-parsable, inverse program based on LR-parsing is derived.…”

Section: Related Work 61 Inverse Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Matsuda

Inaba

Nakano

2012

Higher-Order Symb Comput

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“…Regarding accumulations, studies on left-inverse computation have treated them heuristically [15,26,27] because the injectivity check is usually undecidable with them. Glück and Kawabe [15] uses the LR-parsing technique.…”

Section: Related Work 61 Inverse Computationmentioning

confidence: 99%

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Matsuda

Inaba

Nakano

2012

Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation

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“…The work by Glück and Kawabe [6] (further improved in [7]) presents an automatic program inversion algorithm for first-order functional programs. In contrast to ours, a total inversion algorithm is considered (a particular case of our partial inversion) and, thus, only injective functions produce useful results.…”

Section: Related Workmentioning

confidence: 99%

Automatic Partial Inversion of Inductively Sequential Functions

Almendros-Jiménez

Vidal

Implementation and Application of Functional Languages

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Abstract. We introduce a new partial inversion technique for first-order functional programs. Our technique is simple, fully automatic, and (when it succeeds) returns a program that belongs to the same class of the original program, namely the class of inductively sequential programs (i.e., typical functional programs). To ease the definition, our method proceeds in a stepwise manner: normalization (introduction of let expressions), proper inversion, and removal of let expressions. Furthermore, it can easily be implemented. Therefore, it forms an appropriate basis for developing a practically applicable transformation tool. Preliminary experiments with a prototype implementation of the partial inverter demonstrates the usefulness and viability of our approach.

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Derivation of Deterministic Inverse Programs Based on LR Parsing

Cited by 29 publications

References 11 publications

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Automatic Partial Inversion of Inductively Sequential Functions

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