Abstract Diagnosis of Functional Programs

Alpuente, María; Comini, Marco; Escobar, Santiago; Falaschi, Moreno; Lucas, Salvador

doi:10.1007/3-540-45013-0_1

Cited by 20 publications

(30 citation statements)

References 19 publications

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“…To obtain a manageable analysis, we consider only top-level constructors in the analysis so that a value is some constructor-rooted expression. In principle, this could be extended to any depth bound k (as used in the abstract diagnosis of functional programs [2] or in the abstraction of term rewriting systems [8,9]), but in practice only a depth k = 1 (i.e., top-level constructors) is useful due to the quickly growing size of the abstract domain for k > 1. For instance, for lists we distinguish the values [] (empty list) and ":" (non-empty lists) and for Booleans we distinguish the values True and False.…”

Section: Analysis Of Required Valuesmentioning

confidence: 99%

From Boolean Equalities to Constraints

Antoy

Hanus

2015

Logic-Based Program Synthesis and Transformation

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Abstract. Although functional as well as logic languages use equality to discriminate between logically different cases, the operational meaning of equality is different in such languages. Functional languages reduce equational expressions to their Boolean values, True or False, logic languages use unification to check the validity only and fail otherwise. Consequently, the language Curry, which amalgamates functional and logic programming features, offers two kinds of equational expressions so that the programmer has to distinguish between these uses. We show that this distinction can be avoided by providing an analysis and transformation method that automatically selects the appropriate operation. Without this distinction in source programs, the language design can be simplified and the execution of programs can be optimized. As a consequence, we show that one kind of equational expressions is sufficient and unification is nothing else than an optimization of Boolean equality. MotivationFunctional as well as logic programming languages are based on the common idea to specify computational problems in a high-level and descriptive manner. However, the computational entities and, thus, the programming styles are different. This can be seen in a prominent feature of such languages: the discrimination between logically different cases of a given problem. Functional (as well as imperative) languages use Boolean equations for this purpose, i.e., an equational expression is reduced to either True or False and, depending on the computed result, a different computation path is selected. A typical example is the factorial function where the base case is distinguished from the recursive case by comparing the argument with 0: 1 fac n = if n==0 then 1 else n * fac (n-1) The equality symbol "=" used in this program is different from the Boolean equality "==" above. For instance, in the first rule it is not intended to evaluate X=[] to True or False, but this equality must hold to proceed with this rule, i.e., it is a constraint for subsequent evaluation steps. As a consequence, it is not necessary to fully evaluate equational expressions but one can continue a computation even with partial knowledge, as long as the constraint is ensured to hold. For instance, if we want to ensure that a list L ends with the element 0, we can write Functional logic languages attempt to combine the most important features of functional and logic programming in a single language (see [5,18] for recent surveys). In particular, the functional logic language Curry [21] extends Haskell by common features of logic programming, i.e., non-determinism, free variables, and equational constraints. Due to its roots in functional and logic programming, Curry provides two kinds of equalities: Boolean equality ("==") as in functional programming and equational constraints ("=:=") as in logic programming. The motivation for this decision is to support nested case distinctions, like in functional programming, as well as rule-oriented programming with partial i...

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Section: Analysis Of Required Valuesmentioning

confidence: 99%

From Boolean Equalities to Constraints

Antoy

Hanus

2015

Logic-Based Program Synthesis and Transformation

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“…A debugging technique that is very related to algorithmic debugging is abstract diagnosis [9,10,11]. This technique relies on the same idea as algorithmic debugging but there are two important differences: (1) Abstract diagnosis assumes the existence of a (correct) specification of the program that is being debugged.…”

Section: Algorithmic Debuggingmentioning

confidence: 99%

A survey on algorithmic debugging strategies

Silva

2011

Advances in Engineering Software

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Algorithmic debugging is a debugging technique that has been extended to practically all programming paradigms. Roughly speaking, the technique constructs an internal representation of all (sub)computations performed during the execution of a buggy program; and then, it asks the programmer about the correctness of such computations. The answers of the programmer guide the search for the bug until it is isolated by discarding correct parts of the program. After twenty years of research in algorithmic debugging many different techniques have appeared to improve the original proposal. Surprisingly, no study exists that joins together all these techniques and compares their advantages and their performance. This article presents a study that compares all current algorithmic debugging techniques and analyzes their differences and their costs. The research identifies the dimensions on which each strategy relies. This information allows us to combine the strong points of different strategies.

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“…Consequently, new debugging approaches based on the language's semantics have been introduced in the field of declarative languages, such as abstract diagnosis, which formulates a debugging methodology based on abstract interpretation [9,1], or declarative debugging, also known as algorithmic debugging, which was first introduced by E. Y. Shapiro [19] and that constitutes the framework of this work. Declarative debugging has been widely employed in the logic [10,14,22], functional [21,17,16,18], and multiparadigm [5,3,11] programming languages.…”

Section: Introductionmentioning

confidence: 99%

Declarative Debugging of Membership Equational Logic Specifications

Caballero

Martí-Oliet

Riesco

et al. 2008

Concurrency, Graphs and Models

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Abstract. Algorithmic debugging has been applied to many declarative programming paradigms; in this paper, it is applied to the rewriting paradigm embodied in Maude. We introduce a declarative debugger for executable specifications in membership equational logic which correspond to Maude functional modules. Declarative debugging is based on the construction and navigation of a debugging tree which logically represents the computation steps. We describe the construction of appropriate debugging trees for oriented equational and membership inferences. These trees are obtained as the result of collapsing in proof trees all those nodes whose correctness does not need any justification. We use an extended example to illustrate the use of the declarative debugger and its main features, such as two different strategies to traverse the debugging tree, use of a correct module to reduce the number of questions asked to the user, and selection of trusted vs. suspicious statements by means of labels. The reflective features of Maude have been extensively used to develop a prototype implementation of the declarative debugger for Maude functional modules using Maude itself.

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Abstract Diagnosis of Functional Programs

Cited by 20 publications

References 19 publications

From Boolean Equalities to Constraints

From Boolean Equalities to Constraints

A survey on algorithmic debugging strategies

Declarative Debugging of Membership Equational Logic Specifications

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