LEARNING TO SUPPORT CONSTRAINT PROGRAMMERS

This paper describes the Adaptive Constraint Engine (ACE), an ambitious ongoing research project to support constraint programmers, both human and machine. The program begins with substantial knowledge about constraint satisfaction. The program harnesses a cognitively-oriented architecture (FORR) to manage search heuristics and to learn new ones. ACE can transfer what it learns on simple problems to solve more difficult ones, and can readily export its knowledge to ordinary constraint solvers. It currently serves both as a learner and as a test bed for the constraint community.     

Verifying floating-point programs with constraint programming and abstract interpretation techniques

Static value analysis is a classical approach for verifying programs with floating-point computations. Value analysis mainly relies on abstract interpretation and over-approximates the possible values of program variables. State-of-the-art tools may however compute over-approximations that can be rather coarse for some very usual program expressions. In this paper, we show that constraint solvers can significantly refine approximations computed with abstract interpretation tools. More precisely, we introduce a hybrid approach combining abstract interpretation and constraint programming techniques in a single static and automatic analysis. This hybrid approach benefits from the strong points of abstract interpretation and constraint programming techniques, and thus, it is more effective than static analysers and constraint solvers, when used separately. We compared the efficiency of the system we developed—named rAiCp—with state-of-the-art static analyzers: rAiCp produces substantially more precise approximations and is able to check program properties on both academic and industrial benchmarks.     

Tree Visualisation and Navigation Clues for Information Visualisation

Information visualisation often requires good navigation aids on large trees, which represent the underlying abstract information. Using trees for information visualisation requires novel user interface techniques, visual clues, and navigational aids. This paper describes a visual clue: using the so-called Strahler numbers, a map is provided that indicates which parts of the tree are interesting. A second idea is that of "folding" away subtrees that are too "different" in some sense, thereby reducing the visual complexity of the tree. Examples are given demonstrating these techniques, and what the further challenges in this area are.     

CLPGUI: A Generic Graphical User Interface for Constraint Logic Programming

CLPGUI is a generic graphical user interface for visualizing and controlling the execution of constraint logic programs. CLPGUI has been designed to be used in different contexts: initially for teaching purposes, then for debugging complex programs of real-world scale, and recently for developing end-user interfaces. The challenge of developing a tool which is generic w.r.t. both the constraint logic programming system and the visualizers, is addressed by a client-server architecture for connecting a CLP process to a Java-based GUI process, and by a non-intrusive tracing and control method based on annotations in the CLP program. Arbitrary constraints and goals can be posted incrementally from the GUI in an interactive manner, and arbitrary states can be recomputed. We describe several generic 2D and 3D viewers of the variables and of the search tree, and argue that the 3D representation is best-suited to apprehend the shape of large search trees. We also illustrate the use of CLPGUI for developing application-oriented end-user interfaces on two placement problems, one in virtual reality.     

Cut-and-solve: An iterative search strategy for combinatorial optimization problems

Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems. In this paper, we introduce an iterative search strategy which we refer to as cut-and-solve and prove optimality and termination for this method. This search is different from traditional tree search as there is no branching. At each node in the search path, a relaxed problem and a sparse problem are solved and a constraint is added to the relaxed problem. The sparse problems provide incumbent solutions. When the constraining of the relaxed problem becomes tight enough, its solution value becomes no better than the incumbent solution value. At this point, the incumbent solution is declared to be optimal. This strategy is easily adapted to be an anytime algorithm as an incumbent solution is found at the root node and continuously updated during the search.Cut-and-solve enjoys two favorable properties. Since there is no branching, there are no “wrong” subtrees in which the search may get lost. Furthermore, its memory requirement is negligible. For these reasons, it has potential for problems that are difficult to solve using depth-first or best-first search tree methods.In this paper, we demonstrate the cut-and-solve strategy by implementing a generic version of it for the Asymmetric Traveling Salesman Problem (ATSP). Our unoptimized implementation outperformed state-of-the-art solvers for five out of seven real-world problem classes of the ATSP. For four of these classes, cut-and-solve was able to solve larger (sometimes substantially larger) problems. Our code is available at our websites.     

An Efficient Solution to Systems of Multivariate Polynomial Using Expression Trees

In recent years, several quite successful attempts have been made to solve systems of polynomial constraints, using geometric design tools, exploiting the availability of subdivision-based solvers [7], [11], [12], [15]. This broad range of methods includes both binary domain subdivision as well as the projected polyhedron method of Sherbrooke and Patrikalakis [15]. A prime obstacle in using subdivision solvers is their scalability. When the given constraint is represented as a tensor product of all its independent variables, it grows exponentially in size as a function of the number of variables. In this work, we show that for many applications, especially geometric ones, the exponential complexity of the constraints can be reduced to a polynomial by representing the underlying structure of the problem in the form of expression trees that represent the constraints. We demonstrate the applicability and scalability of this representation and compare its performance to that of tensor product constraint representation through several examples.     

Exploiting subproblem dominance in constraint programming

Many search problems contain large amounts of redundancy in the search. In this paper we examine how to automatically exploit subproblem dominance, which arises when different partial assignments lead to subproblems that dominate (or are dominated by) other subproblems. Subproblem dominance is exploited by caching subproblems that have already been explored by the search, using keys that characterise the subproblems, and failing the search when the current subproblem is dominated by a subproblem already in the cache. In this paper we show how we can automatically and efficiently define keys for arbitrary constraint problems using constraint projection. We show how, for search problems where subproblem dominance arises, a constraint programming solver with this capability can solve these problems orders of magnitude faster than solvers without caching. The system is fully automatic, i.e., subproblem dominance is detected and exploited without any effort from the problem modeller.     

Combining Digital and Traditional Visualisation Techniques in Community-Based Planning and Design

Public participation is a key component in environmental planning and design. Yet too often, architects and planners are not equipped with effective tools and visualisation techniques to generate meaningful public input. Architects and planners are increasingly turning to computer technology; computer imaging can be used to support exploration of alternatives by enabling community members to visualise opportunities and scenarios before committing to a course of action (McClure, 1997). This paper explains how a Geographic Information System (GIS) and an artist played critical roles in a participatory planning process in Chicago's Pilsen neighbourhood. The GIS provided community leaders, planners, architects and designers with an interactive visualisation of the neighbourhood context. It also provided examples of design prototypes in relation to their geographic context. The artist, on the other hand, translated neighbourhood residents' ideas into quick sketches, merging their ideas and thoughts into a shared neighbourhood vision. Both of these elements – the GIS and the human artist – provided a means for residents to visualise past, present, and future neighbourhood conditions, enabling them to have a greater voice in the design of their neighbourhood. Our findings suggest that effective visualisation may be achieved through combining traditional and computerised visualisation tools, and that effective visualisation could assist in bridging the gap between professional designers and non-designers.     

The semantics of augmented constraint diagrams

Constraint diagrams are a diagrammatic notation which may be used to express logical constraints. They generalize Venn diagrams and Euler circles, and include syntax for quantification and navigation of relations. The notation was designed to complement the Unified Modelling Language in the development of software systems.Since symbols representing quantification in a diagrammatic language can be naturally ordered in multiple ways, some constraint diagrams have more than one intuitive meaning in first-order predicate logic. Any equally expressive notation which is based on Euler diagrams and conveys logical statements using explicit quantification will have to address this problem.We explicitly augment constraint diagrams with reading trees, which provides a partial ordering for the quantifiers (determining their scope as well as their relative ordering). Alternative approaches using spatial arrangements of components, or alphabetical ordering of symbols, for example, can be seen as implicit representations of a reading tree.Whether the reading tree accompanies the diagram explicitly (optimizing expressiveness) or implicitly (simplifying diagram syntax), we show how to construct unambiguous semantics for the augmented constraint diagram.     

Exploiting functional dependencies in declarative problem specifications

In this paper we tackle the issue of the automatic recognition of functional dependencies among guessed predicates in constraint problem specifications. Functional dependencies arise frequently in pure declarative specifications, because of the intermediate results that need to be computed in order to express some of the constraints, or due to precise modeling choices, e.g., to provide multiple viewpoints of the search space in order to increase constraint propagation. In either way, the recognition of dependencies greatly helps solvers, allowing them to avoid spending search on unfruitful branches, while maintaining the highest degree of declarativeness. By modeling constraint problem specifications as second-order formulae, we provide a characterization of functional dependencies in terms of semantic properties of first-order ones, and prove undecidability of the problem of their recognition. Despite such negative result, we advocate the (in many cases effective) possibility of using automated tools to mechanize this task. Additionally, we show how suitable search procedures can be automatically synthesized in order to exploit recognized dependencies. We present opl examples of various problems, taken from bio-informatics, planning and resource allocation, and show how in many cases opl greatly benefits from the addition of such search procedures. Moreover, we also give evidence that writing sophisticated ad-hoc search procedures that handle dependencies exploiting the peculiarities of the particular problem is a very difficult and error-prone task which in many cases does not seem to pay-off.     

Low-autocorrelation binary sequences: On improved merit factors and runtime predictions to achieve them

The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence (labs) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length L and return solutions for skew-symmetric binary sequences only – with the consequence that not all best solutions under this constraint will be optimal for each L. In order to improve both, the search for best merit factor and the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search (lssMAts and lssRRts), the third solver (lssOrel) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic average-case performance, lssOrel_8 = 0.000032 * 1.1504^L, has the best chance of finding solutions that improve, as L increases, figures of merit reported to date. The same methodology can be applied to engineering new labs solvers that may return merit factors even closer to the conjectured asymptotic value of 12.3248.     

CPBPV: a constraint-programming framework for bounded program verification

This paper studies how to verify the conformity of a program with its specification and proposes a novel constraint-programming framework for bounded program verification (CPBPV). The CPBPV framework uses constraint stores to represent both the specification and the program and explores execution paths of bounded length nondeterministically. The CPBPV framework detects non-conformities and provides counter examples when a path of bounded length that refutes some properties exists. The input program is partially correct under the boundness restrictions, if each constraint store so produced implies the post-condition. CPBPV does not explore spurious execution paths, as it incrementally prunes execution paths early by detecting that the constraint store is not consistent. CPBPV uses the rich language of constraint programming to express the constraint store. Finally, CPBPV is parameterized with a list of solvers which are tried in sequence, starting with the least expensive and less general. Experimental results often produce orders of magnitude improvements over earlier approaches, running times being often independent of the size of the variable domains. Moreover, CPBPV was able to detect subtle errors in some programs for which other frameworks based on bounded model checking have failed.     

Automating Software Testing Using Program Analysis

During the last 10 years, code inspection for standard programming errors has largely been automated with static code analysis. During the next 10 years, we expect to see similar progress in automating testing, and specifically test generation, thanks to advances in program analysis, efficient constraint solvers, and powerful computers. Three new tools from Microsoft combine techniques from static program analysis, dynamic analysis, model checking, and automated constraint solving while targeting different application domains.     

A constraint-based framework for diagrammatic reasoning

In the near future, visual and diagrammatic human-computer interfaces, such as those of UML-based CASE tools, will be required to offer a much more intelligent behavior than just editing. Yet there is very little formal support and there are almost no tools available for the construction of intelligent diagrammatic environments. The present paper introduces a constraint-based formalism for the specification and implementation of complex diagrammatic environments. We start from grammar-based definitions of diagrammatic languages and show how a constraint solver for diagram recognition and interpretation can automatically be constructed from such grammars. In a second step, the capabilities of these solvers are extended by allowing to axiomatise formal diagrammatic systems so that they can be regarded as a new constraint domain. The ultimate aim of this schema is to establish a constraint logic language for diagrammatic reasoning applications.     

A modular package manager architecture

ContextThe success of modern software distributions in the Free and Open Source world can be explained, among other factors, by the availability of a large collection of software packages and the possibility to easily install and remove those components using state-of-the-art package managers. However, package managers are often built using a monolithic architecture and hard-wired and ad-hoc dependency solvers implementing some customized heuristics.ObjectiveWe aim at laying the foundation for improving on existing package managers. Package managers should be complete, that is find a solution whenever there exists one, and allow the user to specify complex criteria that define how to pick the best solution according to the user’s preferences.MethodIn this paper we propose a modular architecture relying on precise interface formalisms that allows the system administrator to choose from a variety of dependency solvers and backends.ResultsWe have built a working prototype–called MPM–following the design advocated in this paper, and we show how it largely outperforms a variety of current package managers.ConclusionWe argue that a modular architecture, allowing for delegating the task of constraint solving to external solvers, is the path that leads to the next generation of package managers that will deliver better results, offer more expressive preference languages, and be easily adaptable to new platforms.     

An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks

Much of software developers' time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse's navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse's package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner     

Automatic Implication Checking for CHR Constraints

Constraint Handling Rules (CHRs) are a high-level rule-based programming language commonly used to define constraint solvers. We present a method for automatic implication checking between constraints of CHR solvers. Supporting implication is important for implementing extensible solvers and reification, and for building hierarchical CHR constraint solvers. Our method does not copy the entire constraint store, but performs the check in place using a trailing mechanism. The necessary code enhancements can be done by automatic program transformation based on the rules of the solver. We extend our method to work for hierarchically organized modular CHR solvers. We show the soundness of our method and its completeness for a restricted class of canonical solver as well as for specific existing non-canonical CHR solvers. We evaluate our trailing method experimentally by comparing with the copy approach: runtime is almost halved.     

Untangling the balancing and searching of balanced binary search trees

A balanced binary search tree can be characterized by two orthogonal issues: its search strategy and its balancing strategy. In this paper, we show how to decouple search and balancing strategies so that they can be expressed independently of each other, communicating only by basic operations such as rotations. Different balancing strategies, such as red–black trees and splay trees, and different search applications, such as key search and rank search, can be combined arbitrarily. As a new result, we show how optimal string search can be expressed as a search application on any balanced binary tree. We implement our framework in C++, with the heavy use of templates and inlining. It keeps balancing and searching separate, while still delivering a performance that compares favorably to widely used binary tree implementations. Common services, such as correctness checks and timing measurements, do not have to be rewritten for each tree implementation. The common framework simplifies experimentation with trees and search algorithms; as a demonstration, we present some simple comparisons of red–black trees, splay trees and treaps. Copyright © 2003 John Wiley & Sons, Ltd.