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ACE: an automatic complexity evaluator

Published: 01 April 1988 Publication History

Abstract

There has been a great deal of research done on the evaluation of the complexity of particular algorithms; little effort, however, has been devoted to the mechanization of this evaluation. The ACE (Automatic Complexity Evaluator) system is able to analyze reasonably large programs, like sorting programs, in a fully mechanical way. A time-complexity function is derived from the initial functional program. This function is transformed into its nonrecursive equivalent according to MacCarthy's recursion induction principle, using a predefined library of recursive definitions. As the complexity is not a decidable property, this transformation will not be possible in all cases. The richer the predefined library is, the more likely the system is to succeed. The operations performed by ACE are described and the use of the system is illustrated with the analysis of a sorting algorithm. Related works and further improvements are discussed in the conclusion.

References

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Reviews

D. John Cooke

The ACE system provides a partial procedure for finding the worst-case complexity of FP programs. The notion of complexity used is essentially the (order of magnitude of the) number of recursive calls necessary for the program evaluation; as such, the complexity is expressed as a composite of standard functions such as exponentials and logarithms. Having such a measure facilitates comparison of different equivalent programs such as might be obtained by a program transformation system. This is paradoxical because ACE uses the FP algebra to perform transformations and simplifications of Cf, the complexity function for the program under investigation ( Cf is derived by a straightforward syntax-directed translation from the given FP program, f). Subsequently, recursion induction is used to extract a nonrecursive function. Other topics that the reader will encounter include fixpoint theory, folding, computational induction, and the splitting of complex conditionals into a set of simpler forms that each involve a single condition (following McCarthy). Unfortunately, as complexity is inexorably related to termination and termination is undecidable, the system does not always successfully yield a complexity function for a given program. Adding more transformations, more information on `standard' functions, and better approximating functions extends the set of programs on which ACE will succeed. The addition of more rules within the system, however, makes the rule selection process harder and the system slower—but that's another problem. The paper flows well; it consists of two introductory sections, two technical sections, a sizable example (example 3 of section 5 includes illustrations of most of the technical points mentioned earlier), and a conclusion. There is little comparable material in the literature, but all related background is adequately cross-referenced for those who need to brush up on their theory. The ACE system, emanating from ESPRIT project 302 (funded by the EEC), is a very neat application of established theory that is used to harness the emerging technology of program transformation. The use of the FP algebra is central to the methodology, but extensions to other similar languages are clearly possible by using FP as an internal/intermediate form. There are a few bothersome typographical errors and other minor irritations (most notably `verify' for `satisfy,' lower case `o' for the function composition symbol 3:9D, and :3WCfor the atom that is the empty sequence rather than &fgr; as used by Backus in his original FP paper) but all in all this is a very readable paper describing interesting and useful work.

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Information & Contributors

Information

Published In

cover image ACM Transactions on Programming Languages and Systems
ACM Transactions on Programming Languages and Systems  Volume 10, Issue 2
April 1988
154 pages
ISSN:0164-0925
EISSN:1558-4593
DOI:10.1145/42190
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 1988
Published in TOPLAS Volume 10, Issue 2

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Cited By

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  • (2024)A Machine Learning-Based Approach for Solving Recurrence Relations and Its use in Cost Analysis of Logic ProgramsTheory and Practice of Logic Programming10.1017/S1471068424000413(1-45)Online publication date: 21-Nov-2024
  • (2023)A Class of Programs that Admit Exact Complexity Analysis via Newton?s Polynomial InterpolationProceedings of the XXVII Brazilian Symposium on Programming Languages10.1145/3624309.3624311(50-55)Online publication date: 25-Sep-2023
  • (2022)Modeling Asymptotic Complexity Using ACL2Electronic Proceedings in Theoretical Computer Science10.4204/EPTCS.359.9359(83-98)Online publication date: 24-May-2022
  • (2022)Denotational semantics as a foundation for cost recurrence extraction for functional languagesJournal of Functional Programming10.1017/S095679682200003X32Online publication date: 5-Jul-2022
  • (2021)An Inversion Tool for Conditional Term Rewriting Systems - A Case Study of Ackermann InversionElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.341.3341(33-41)Online publication date: 6-Sep-2021
  • (2021)Mechanisation of the AKS AlgorithmJournal of Automated Reasoning10.1007/s10817-020-09563-y65:2(205-256)Online publication date: 1-Feb-2021
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  • (2019)Amortized Complexity VerifiedJournal of Automated Reasoning10.1007/s10817-018-9459-362:3(367-391)Online publication date: 1-Mar-2019
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