A polyvariant binding-time analysis for off-line partial deduction

Bruynooghe, Maurice; Leuschel, Michaël; Sagonas, Konstantinos

doi:10.1007/bfb0053561

Cited by 18 publications

(28 citation statements)

References 24 publications

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“…Determinacy inference can address this issue and it seems promising as a form of binding-time analysis in the so-called semi-online (or mixline) approach to program specialisation [13]. In this scheme, the usual static and dynamic polarisation that is used within classic binding-time analysis [4] is refined by adding an additional binding-type semi. As well as always unfolding a static call and never unfolding a dynamic call, the unfolding decision for a semi call is postponed until specialisation time.…”

Section: Introductionmentioning

confidence: 99%

Determinacy Inference for Logic Programs

King

2005

Programming Languages and Systems

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Abstract. This paper presents a determinacy inference analysis for logic programs. The analysis infers determinacy conditions that, if satisfied by a call, ensures that it computes at most one answer and that answer is generated only once. The principal component of the technique is a goalindependent analysis of individual clauses. This derives a condition for a call that ensures only one clause in the matching predicate possesses a successful derivation. Another key component of the analysis is backwards reasoning stage that strengthens these conditions to derive properties on a call that assure determinacy. The analysis has applications in program development, implementation and specialisation.

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Section: Introductionmentioning

confidence: 99%

Determinacy Inference for Logic Programs

King

2005

Programming Languages and Systems

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“…Offline approaches to local control of partial deduction on the other hand [38,22,23,10] have been very limited in other respects. Specifically, each atom in the body of a clause is marked as either reducible or non-reducible.…”

Section: Introductionmentioning

confidence: 99%

“…Such goals are very common in logic programming, and the key issue which needs to be considered is termination. A partial solution to this problem has been presented in [10], but it still sticks with the limited unfolding mentioned above and can "only" handle a certain class of partially instantiated data (data bounded wrt some semi-linear norm).…”

Section: Introductionmentioning

confidence: 99%

Sonic Partial Deduction

Martin

Leuschel

2000

Lecture Notes in Computer Science

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Abstract. The current state of the art for ensuring finite unfolding of logic programs consists of a number of online techniques where unfolding decisions are made at specialisation time. Introduction of a static termination analysis phase into a partial deduction algorithm permits unfolding decisions to be made offline, before the actual specialisation phase itself. This separation improves specialisation time and facilitates the automatic construction of compilers and compiler generators. The main contribution of this paper is how this separation may be achieved in the context of logic programming, while providing non-trivial support for partially static datastructures. The paper establishes a solid link between the fields of static termination analysis and partial deduction enabling existing termination analyses to be used to ensure finiteness of the unfolding process. This is the first offline technique which allows arbitrarily partially instantiated goals to be sufficiently unfolded to achieve good specialisation results. Furthermore, it is demonstrated that an offline technique such as this one can be implemented very efficiently and, surprisingly, yield even better specialisation than a (pure) online technique. It is also, to our knowledge, the first offline approach which passes the KMP test (i.e., obtaining an efficient Knuth-Morris-Pratt pattern matcher by specialising a naive one).

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“…This means that all calls to built-ins are explicitly checked by the watchdog, and the filter errors are also caught and presented to the user. 5 Another common mistake relates to backpropagation of bindings [26] in the context of non-logical built-ins and connectives. Here the watchdog uses co-routining to detect those backpropagations.…”

Section: Methodsmentioning

confidence: 99%

“…The main difficulty lies in coming up with the correct annotations (and then maintaining them as the source program evolves). Indeed, while some errors (i.e., annotating an argument as static even though it is dynamic) can be easily identified by various abstract interpretation schemes (see, e.g., [5,8]), ensuring termination of the specialisation process is a major obstacle. Recent work has led to a fully automatic BTA [8], but unfortunately the BTA still only provides partial termination guarantees 2 ; is sometimes overly conservative, especially for the more involved (and more interesting) applications; and can be too costly to apply for larger, real-life Prolog programs.…”

Section: Introductionmentioning

confidence: 99%

Supervising Offline Partial Evaluation of Logic Programs Using Online Techniques

Leuschel

Craig

Elphick

Logic-Based Program Synthesis and Transformation

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Abstract. A major impediment for more widespread use of offline partial evaluation is the difficulty of obtaining and maintaining annotations for larger, realistic programs. Existing automatic binding-time analyses still only have limited applicability and annotations often have to be created or improved and maintained by hand, leading to errors. We present a technique to help overcome this problem by using online control techniques which supervise the specialisation process in order to detect such errors. We discuss an implementation in the logen system and show on a series of examples that this approach is effective: very few false alarms were raised while infinite loops were detected quickly. We also present the integration of this technique into a web interface, which highlights problematic annotations directly in the source code. A method to automatically fix incorrect annotations is presented, allowing the approach to be also used as a pragmatic binding time analysis. Finally we show how our method can be used for efficiently locating errors with built-ins inside Prolog source code.

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A polyvariant binding-time analysis for off-line partial deduction

Cited by 18 publications

References 24 publications

Determinacy Inference for Logic Programs

Determinacy Inference for Logic Programs

Sonic Partial Deduction

Supervising Offline Partial Evaluation of Logic Programs Using Online Techniques

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