24,601 research outputs found
An Environment for Analyzing Space Optimizations in Call-by-Need Functional Languages
We present an implementation of an interpreter LRPi for the call-by-need
calculus LRP, based on a variant of Sestoft's abstract machine Mark 1, extended
with an eager garbage collector. It is used as a tool for exact space usage
analyses as a support for our investigations into space improvements of
call-by-need calculi.Comment: In Proceedings WPTE 2016, arXiv:1701.0023
Improving Prolog Programs: Refactoring for Prolog
Refactoring is an established technique from the OO-community to restructure
code: it aims at improving software readability, maintainability and
extensibility. Although refactoring is not tied to the OO-paradigm in
particular, its ideas have not been applied to Logic Programming until now.
This paper applies the ideas of refactoring to Prolog programs. A catalogue
is presented listing refactorings classified according to scope. Some of the
refactorings have been adapted from the OO-paradigm, while others have been
specifically designed for Prolog. Also the discrepancy between intended and
operational semantics in Prolog is addressed by some of the refactorings.
In addition, ViPReSS, a semi-automatic refactoring browser, is discussed and
the experience with applying \vipress to a large Prolog legacy system is
reported. Our main conclusion is that refactoring is not only a viable
technique in Prolog but also a rather desirable one.Comment: To appear in ICLP 200
Improving Prolog programs: Refactoring for Prolog
Refactoring is an established technique from the object-oriented (OO)
programming community to restructure code: it aims at improving software
readability, maintainability and extensibility. Although refactoring is not
tied to the OO-paradigm in particular, its ideas have not been applied to Logic
Programming until now.
This paper applies the ideas of refactoring to Prolog programs. A catalogue
is presented listing refactorings classified according to scope. Some of the
refactorings have been adapted from the OO-paradigm, while others have been
specifically designed for Prolog. The discrepancy between intended and
operational semantics in Prolog is also addressed by some of the refactorings.
In addition, ViPReSS, a semi-automatic refactoring browser, is discussed and
the experience with applying ViPReSS to a large Prolog legacy system is
reported. The main conclusion is that refactoring is both a viable technique in
Prolog and a rather desirable one.Comment: To appear in Theory and Practice of Logic Programming (TPLP
Transformations of Logic Programs with Goals as Arguments
We consider a simple extension of logic programming where variables may range
over goals and goals may be arguments of predicates. In this language we can
write logic programs which use goals as data. We give practical evidence that,
by exploiting this capability when transforming programs, we can improve
program efficiency.
We propose a set of program transformation rules which extend the familiar
unfolding and folding rules and allow us to manipulate clauses with goals which
occur as arguments of predicates. In order to prove the correctness of these
transformation rules, we formally define the operational semantics of our
extended logic programming language. This semantics is a simple variant of
LD-resolution. When suitable conditions are satisfied this semantics agrees
with LD-resolution and, thus, the programs written in our extended language can
be run by ordinary Prolog systems.
Our transformation rules are shown to preserve the operational semantics and
termination.Comment: 51 pages. Full version of a paper that will appear in Theory and
Practice of Logic Programming, Cambridge University Press, U
A heuristic-based approach to code-smell detection
Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache
Evolutionary improvement of programs
Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues
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