Constraint Solving on Bounded String Variables

Scott, Joseph D.; Flener, Pierre; Pearson, Justin

doi:10.1007/978-3-319-18008-3_26

Cited by 9 publications

(16 citation statements)

References 34 publications

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“…Given that we now have string variables, inspired by [23,24], we introduce the string constraints specified in Table 1. The constraints =, =, ≺, , , have the semantics of their standard definitions.…”

Section: Minizinc With Stringsmentioning

confidence: 99%

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MiniZinc with Strings

Amadini

Flener

Pearson

et al. 2017

Logic-Based Program Synthesis and Transformation

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Strings are extensively used in modern programming languages and constraints over strings of unknown length occur in a wide range of real-world applications such as software analysis and verification, testing, model checking, and web security. Nevertheless, practically no CP solver natively supports string constraints. We introduce string variables and a suitable set of string constraints as builtin features of the MiniZinc modelling language. Furthermore, we define an interpreter for converting a MiniZinc model with strings into a FlatZinc instance relying on only integer variables. This provides a user-friendly interface for modelling combinatorial problems with strings, and enables both string and non-string solvers to actually solve such problems.

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Section: Minizinc With Stringsmentioning

confidence: 99%

“…Varr(X, |x|, 0..|x|), regular(X, q, s, D , q0, F ), [24]. However, we remark that this decomposition is only one of the possible choices for solving a CP problem with strings, implemented here for compatibility with solvers that support FlatZinc and naturally handle integer variables.…”

Section: (1)mentioning

confidence: 99%

MiniZinc with Strings

Amadini

Flener

Pearson

et al. 2017

Logic-Based Program Synthesis and Transformation

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“…• We fully model ES6 regex in terms of classical regular languages and string constraints ( §4) and cover several aspects missing from previous work [27,29,30]. We introduce the notion of a capturing language to make the problem of matching and capture group assignment self-contained.…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty arises from the typical mixing of constraints in path conditionssimply generating a matching word for a standalone regex is easy (without lookaheads). To date, there has been only limited progress on this problem, mostly addressing immediate needs of implementations with approximate solutions, e.g., for capture groups [29] and backreferences [27,30]. However, neither matching precedence nor lookaheads have been addressed before.…”

Section: Introductionmentioning

confidence: 99%

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Sound regular expression semantics for dynamic symbolic execution of JavaScript

Loring

Mitchell

Kinder

2019

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

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Support for regular expressions in symbolic execution-based tools for test generation and bug finding is insufficient. Common aspects of mainstream regular expression engines, such as backreferences or greedy matching, are ignored or imprecisely approximated, leading to poor test coverage or missed bugs. In this paper, we present a model for the complete regular expression language of ECMAScript 2015 (ES6), which is sound for dynamic symbolic execution of the test and exec functions. We model regular expression operations using string constraints and classical regular expressions and use a refinement scheme to address the problem of matching precedence and greediness. We implemented our model in ExpoSE, a dynamic symbolic execution engine for JavaScript, and evaluated it on over 1,000 Node.js packages containing regular expressions, demonstrating that the strategy is effective and can significantly increase the number of successful regular expression queries and therefore boost coverage.CCS Concepts • Software and its engineering → Software verification and validation; Dynamic analysis; • Theory of computation → Regular languages.

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Design and Implementation of Bounded-Length Sequence Variables

Scott

Flener

Pearson

et al. 2017

Integration of AI and OR Techniques in Constraint Programming

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We present the design and implementation of bounded-length sequence (BLS) variables for a CP solver. The domain of a BLS variable is represented as the combination of a set of candidate lengths and a sequence of sets of candidate characters. We show how this representation, together with requirements imposed by propagators, affects the implementation of BLS variables for a copying CP solver, most importantly the closely related decisions of data structure, domain restriction operations, and propagation events. The resulting implementation outperforms traditional bounded-length string representations for CP solvers, which use a fixed-length array of candidate characters and a padding symbol.

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Constraint Solving on Bounded String Variables

Cited by 9 publications

References 34 publications

MiniZinc with Strings

MiniZinc with Strings

Sound regular expression semantics for dynamic symbolic execution of JavaScript

Design and Implementation of Bounded-Length Sequence Variables

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