The Sat4j library, release 2.2

Berre, Daniel Le; Parrain, Anne

doi:10.3233/sat190075

Cited by 315 publications

(204 citation statements)

References 11 publications

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“…We have implemented the proposed synthesis technique in a new tool called Migrator, which is implemented in Java. Migrator uses the Sat4J solver [31] for answering all SAT and MaxSAT queries and the Mediator tool [54] for verifying equivalence between a pair of database programs.…”

Section: Methodsmentioning

confidence: 99%

Synthesizing database programs for schema refactoring

Wang

Dong

Shah

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Many programs that interact with a database need to undergo schema refactoring several times during their life cycle. Since this process typically requires making significant changes to the program's implementation, schema refactoring is often non-trivial and error-prone. Motivated by this problem, we propose a new technique for automatically synthesizing a new version of a database program given its original version and the source and target schemas. Our method does not require manual user guidance and ensures that the synthesized program is equivalent to the original one. Furthermore, our method is quite efficient and can synthesize new versions of database programs (containing up to 263 functions) that are extracted from real-world web applications with an average synthesis time of 69.4 seconds.CCS Concepts • Software and its engineering → Programming by example; Automatic programming; • Information systems → Database utilities and tools.

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Section: Methodsmentioning

confidence: 99%

Synthesizing database programs for schema refactoring

Wang

Dong

Shah

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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“…Sugar translates a CSP instance to an instance of the Boolean satisfiability problem (SAT). SAT4J is a SAT solver [11]. Sugar and SAT4J are both written in Java.…”

Section: Methodsmentioning

confidence: 99%

Finding Minimum Locating Arrays Using a SAT Solver

Konishi

Kojima

Nakagawa

et al. 2017

2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Combinatorial interaction testing is an efficient software testing strategy. If all interactions among test parameters or factors needed to be covered, the size of a required test suite would be prohibitively large. In contrast, this strategy only requires covering t-wise interactions where t is typically very small. As a result, it becomes possible to significantly reduce test suite size. Locating arrays aim to enhance the ability of combinatorial interaction testing. In particular, (1, t)-locating arrays can not only execute all t-way interactions but also identify, if any, which of the interactions causes a failure. In spite of this useful property, there is only limited research either on how to generate locating arrays or on their minimum sizes. In this paper, we propose an approach to generating minimum locating arrays. In the approach, the problem of finding a locating array consisting of N tests is represented as a Constraint Satisfaction Problem (CSP) instance, which is in turn solved by a modern CSP solver. The results of using the proposed approach reveal many (1, t)-locating arrays that are smallest known so far. In addition, some of these arrays are proved to be minimum.

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“…As our proposed approach in this work (which based upon continuous optimization) differs quite substantially from these approaches, we refer the reader to a recent survey (Morgado et al, 2013) for much more detailed descriptions about the current state of the art. However, broadly speaking, there have been two main classes for these discrete solvers: 1) those based upon bounding the solution via SAT method (Marques-Sila and Planes, 2011;Koshimura et al, 2012;Ansótegui, Bonet, and Levy, 2013;Fu and Malik, 2006;Le Berre and Parrain, 2010;Eén and Sorensson, 2006) which in turn exploit the heuristic developed by the SAT community such as those in the MiniSAT solver; these solvers typically are complete in that they will both produce a satisfying assignment with some number of clauses satisfied and a verification that this is the optimal solution to the problem. And 2) those based upon local search (Luo et al, 2015(Luo et al, , 2017, which maintain and locally adjust a solution to satisfy an increasing number of clauses; these solvers typically are incomplete in that they may quickly find an assignment, but often cannot prove whether or not it is optimal.…”

Section: Discrete Maxsat Solversmentioning

confidence: 99%

Low-Rank Semidefinite Programming for the MAX2SAT Problem

Wang

Kolter

2019

AAAI

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This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches. Semidefinite programming techniques are well-known as a theoretical tool for approximating maximum satisfiability problems, but their application has traditionally been very limited by their speed and randomized nature. Our approach overcomes this difficult by using a recent approach to low-rank semidefinite programming, specialized to work in an incremental fashion suitable for use in an exact search algorithm. The method can be used both within complete or incomplete solver, and we demonstrate on a variety of problems from recent competitions. Our experiments show that the approach is faster (sometimes by orders of magnitude) than existing state-of-the-art complete and incomplete solvers, representing a substantial advance in search methods specialized for MAX2SAT problems.

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The Sat4j library, release 2.2

Cited by 315 publications

References 11 publications

Synthesizing database programs for schema refactoring

Synthesizing database programs for schema refactoring

Finding Minimum Locating Arrays Using a SAT Solver

Low-Rank Semidefinite Programming for the MAX2SAT Problem

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