物件導向軟體之架構(Object-Oriented Software Construction)探討

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Functions, types and expressions programming languages and their operational semantics compilation partial evaluation of a flow chart languages partial evaluation of a first-order functional languages the view from Olympus partial evaluation of the Lambda calculus partial evaluation of prolog aspects of Similix - a partial evaluator for a subset of scheme partial evaluation of C applications of partial evaluation termination of partial evaluation program analysis more general program transformation guide to the literature the self-applicable scheme specializer.

Partial evaluation and automatic program generation

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.

The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.

The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.

- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications

Table of Contents

Foreword (Ugo Montanari) 
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) 
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) 
Chapter 3. Constraint Propagation (Christian Bessiere) 
Chapter 4. Backtracking Search Algorithms (Peter van Beek) 
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) 
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons) 
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) 
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith) 
Part II : Extensions, Languages, and Applications

Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) 
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) 
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) 
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) 
Chapter 17. Constraints over Structured Domains
(Carmen Gervet) 
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings) 
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) 
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

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Handbook of Constraint Programming

Hofmann and Jost have presented a heap space analysis [1] that finds linear space bounds for many functional programs. It uses an amortised analysis: assigning hypothetical amounts of free space (called potential) to data structures in proportion to their sizes using type annotations. Constraints on these annotations in the type system ensure that the total potential assigned to the input is an upper bound on the total memory required to satisfy all allocations.

We describe a related system for bounding the stack space requirements which uses the depth of data structures, by expressing potential in terms of maxima as well as sums. This is achieved by adding extra structure to typing contexts (inspired by O'Hearn's bunched typing [2]) to describe the form of the bounds. We will also present the extra steps that must be taken to construct a typing during the analysis.

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Amortised Memory Analysis Using the Depth of Data Structures

Embedded systems often come with constrained memory footprints. It is therefore essential to ensure that software running on such platforms fulfils memory usage specifications at compile-time, to prevent memory-related software failure after deployment. Previous proposals on memory usage verification are not satisfactory as they usually can only handle restricted subsets of programs, especially when shared mutable data structures are involved. In this paper, we propose a simple but novel solution. We instrument programs with explicit memory operations so that memory usage verification can be done along with the verification of other properties, using an automated verification system Hip/Sleek developed recently by Chin et al.[10,19]. The instrumentation can be done automatically and is proven sound with respect to an underlying semantics. One immediate benefit is that we do not need to develop from scratch a specific system for memory usage verification. Another benefit is that we can verify more programs, especially those involving shared mutable data structures, which previous systems failed to handle, as evidenced by our experimental results.

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Memory Usage Verification Using Hip/Sleek

In this paper, we present an approach to find upper bounds of heap space for Java Card applets. Our method first transforms an input bytecode stream into a control flow graph (CFG), and then collapses cycles of the CFG to produce a directed acyclic graph (DAG). Based on the DAG, we propose a linear-time algorithm to solve the problem of finding the single-source largest path in it. We also have implemented a prototype tool, tested it on several sample applications, and then compared the bounds found by our tool with the actual heap bounds of the programs. The experiment shows that our tool returns good estimation of heap bounds, runs fast, and has a small memory footprint.

/pdf/a-fast-algorithm-to-compute-heap-memory-bounds-of-java-card-mqzt9dzhsy.pdf

A Fast Algorithm to Compute Heap Memory Bounds of Java Card Applets

In the current work, we investigate the benefits of immutability guarantees for allowing more flexible handling of aliasing, as well as more precise and concise specifications. Our approach supports finer levels of control that can mark data structures as being immutable through the use of immutability annotations. By using such annotations to encode immutability guarantees, we expect to obtain better specifications that can more accurately describe the intentions, as well as prohibitions, of the method. Ultimately, our goal is improving the precision of the verification process, as well as making the specifications more readable, more precise and as an enforceable program documentation. We have designed and implemented a new entailment procedure to formally and automatically reason about immutability enhanced specifications. We have also formalised the soundness for our new procedure through an operational semantics with mutability assertions on the heap. Lastly, we have carried out a set of experiments to both validate and affirm the utility of our current proposal on immutability enhanced specification mechanism.

Immutable specifications for more concise and precise verification

We present an error calculus to support a novel specification mechanism for sound and/or complete safety properties that are to be given by users. With such specifications, our calculus can form a foundation for both proving program safety and/or discovering real bugs. The basis of our calculus is an algebra with a lattice domain of four abstract statuses (namely unreachability, validity, must-error and may-error) on possible program states and four operators for this domain to calculate suitable program status.We show how proof search and error localization can be supported by our calculus. Our calculus can also be extended to separation logic with support for user-defined predicates and lemmas.We have implemented our calculus in an automated verification tool for pointer-based programs. Initial experiments have confirmed that it can achieve the dual objectives, namely of safety proving and bug finding, with modest overheads.

/pdf/towards-complete-specifications-with-an-error-calculus-14pm0ndrqr.pdf

Towards Complete Specifications with an Error Calculus

Separation logic-based abstraction mechanisms, enhanced with userdefined inductive predicates, represent a powerful, expressive means of specifying heap-based data structures with strong invariant properties. However, expressive power comes at a cost: the manipulation of such logics typically requires the unfolding of disjunctive predicates which may lead to expensive proof search. We address this problem by proposing a predicate specialization technique that allows efficient symbolic pruning of infeasible disjuncts inside each predicate instance. Our technique is presented as a calculus whose derivations preserve the satisfiability of formulas, while reducing the subsequent cost of their manipulation. Initial experimental results have confirmed significant speed gains from the deployment of predicate specialization. While specialization is a familiar technique for code optimization, its use in program verification is new.

/pdf/a-specialization-calculus-for-pruning-disjunctive-predicates-4tfw4pdb92.pdf

Wei-Ngan Chin

Papers

Memory Usage Verification Using Hip/Sleek

A Fast Algorithm to Compute Heap Memory Bounds of Java Card Applets

Immutable specifications for more concise and precise verification

Towards Complete Specifications with an Error Calculus

A specialization calculus for pruning disjunctive predicates to support verification