Analyzing Context-Free Grammars Using an Incremental SAT Solver

Axelsson, Roland; Heljanko, Keijo; Lange, Martin

doi:10.1007/978-3-540-70583-3_34

Cited by 44 publications

(48 citation statements)

References 12 publications

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“…-First we built confidence in our baseline sentence generator by comparing it to the external sentence generators AMBER [14] and CFGANALYZER [1]. For this we used a grammar collection also used in [3], which contains 87 small toy grammars and 25 large grammars of real-world programming languages.…”

Section: Validationmentioning

confidence: 99%

Ambiguity Detection: Scaling to Scannerless

Basten

Klint

Vinju

2012

Software Language Engineering

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Abstract. Static ambiguity detection would be an important aspect of language workbenches for textual software languages. However, the challenge is that automatic ambiguity detection in context-free grammars is undecidable in general. Sophisticated approximations and optimizations do exist, but these do not scale to grammars for so-called "scannerless parsers", as of yet. We extend previous work on ambiguity detection for context-free grammars to cover disambiguation techniques that are typical for scannerless parsing, such as longest match and reserved keywords. This paper contributes a new algorithm for ambiguity detection in character-level grammars, a prototype implementation of this algorithm and validation on several real grammars. The total run-time of ambiguity detection for character-level grammars for languages such as C and Java is significantly reduced, without loss of precision. The result is that efficient ambiguity detection in realistic grammars is possible and may therefore become a tool in language workbenches.

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Section: Validationmentioning

confidence: 99%

Ambiguity Detection: Scaling to Scannerless

Basten

Klint

Vinju

2012

Software Language Engineering

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“…We evaluated our algorithm by comparing our prototype implementation to publicly available tools like CFGA [54], DPRLE (Chapter 2), Rex [42] (also featured in Chapter 3), and Hampi [39].…”

Section: Resultsmentioning

confidence: 99%

“…A comprehensive performance comparison between our prototype tool and existing implementations. We compare against CFG Analyzer [54], DPRLE (Chapter 2), Rex [42], and Hampi [39]. We use several sets of established benchmarks [39,42].…”

Section: Chapter 4 Solving String Constraints Lazilymentioning

confidence: 99%

“…For each n between 1 and 1000 inclusive, the task is to compute a string in the intersection of • In Section 4.2.4, we compare CFG Analyzer, Hampi, and our prototype on a grammar intersection task. We select 85 pairs of context-free grammars from a large data set [54]. The task, for each implementation, is to generate strings of length 5, 10, and 12, for each grammar pair.…”

Section: Regular Expressionmentioning

confidence: 99%

“…In this experiment, we compare the performance of CFG Analyzer (CFGA) [54], Hampi [39], and we randomly select pairs of grammars and have each tool search for a string in the intersection for several length bounds.…”

Section: Experiments 4: Length-bounded Context-free Intersectionmentioning

confidence: 99%

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Decision Procedures for String Constraints

Hooimeijer¹

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String-related defects are among the most prevalent and costly in modern software development. For example, in terms of frequency, cross-site scripting vulnerabilities have long surpassed traditional exploits like buffer overruns. The state of this problem is particularly disconcerting because it does not just affect legacy code: developing web applications today -even when adhering to best practices and using modern library support -remains error-prone.A number of program analysis approaches aim to prevent or mitigate string-related defects; examples include static bug detectors and automated testcase generators. Traditionally, this work has relied on built-in algorithms to reason about string-manipulating code. This arrangement is suboptimal for two reasons: first, it forces researchers to re-invent the wheel for each new analysis; and second, it does not encourage the independent improvement of domain-specific algorithms for handling strings.In this dissertation, we present research on specialized decision algorithms for string constraints.Our high-level approach is to provide a constraint solving interface; a client analysis can use that interface to reason about strings in the same way it might use a SAT solver to reason about binary state. To this end, we identify a set of string constraints that captures common programming language constructs, and permits efficient solving algorithms. We provide a core solving algorithm together with a machine-checkable proof of its correctness.Next, we focus on performance. We evaluate a variety of datastructures and algorithms in a controlled setting to inform our choice of each. Our final approach is based on two insights: (1) string constraints can be cast as an explicit search problem, and (2) to solve these constraints, we can i instantiate the search space lazily through incremental refinement. These insights lead to substantial performance gains relative to competing approaches; our experimental results show our prototype to be several of magnitude faster across several published benchmarks. iv Chapter 1 IntroductionThis dissertation focuses on a common source of software defects: string manipulation. Stringrelated defects are among the most prevalent and costly in modern software development. Reasoning about strings is a key aspect in many types of program analysis work, including static bug finding [1, 5, 6,7] and automated testing [8,9,10,11,12]. Until recently, this work has relied on ad hoc algorithms to formally reason about the values that string variables may take at runtime.That situation is suboptimal for two reasons: first, it forces researchers to re-invent the wheel for each new tool; and second, it does not encourage the independent improvement of domain-specific reasoning for strings.In this dissertation, we focus on the development of algorithms that enable formal reasoning about string operations; we refer to these algorithms as string decision procedures. Informally, a decision procedure is an algorithm that, given an input formula, answe...

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A Decision Procedure for String Logic with Quadratic Equations, Regular Expressions and Length Constraints

2018

Programming Languages and Systems

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In this work, we consider the satisfiability problem in a logic that combines word equations over string variables denoting words of unbounded lengths, regular languages to which words belong and Presburger constraints on the length of words. We present a novel decision procedure over two decidable fragments that include quadratic word equations (i.e., each string variable occurs at most twice). The proposed procedure reduces the problem to solving the satisfiability in the Presburger arithmetic. The procedure combines two main components: (i) an algorithm to derive a complete set of all solutions of conjunctions of word equations and regular expressions; and (ii) two methods to precisely compute relational constraints over string lengths implied by the set of all solutions. We have implemented a prototype tool and evaluated it over a set of satisfiability problems in the logic. The experimental results show that the tool is effective and efficient.Concrete string models assume a finite alphabet Σ whose elements are called letters, set of finite words over Σ * including ǫ -the empty word, and a set of integer numbers Z. We

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Analyzing Context-Free Grammars Using an Incremental SAT Solver

Cited by 44 publications

References 12 publications

Ambiguity Detection: Scaling to Scannerless

Ambiguity Detection: Scaling to Scannerless

Decision Procedures for String Constraints

A Decision Procedure for String Logic with Quadratic Equations, Regular Expressions and Length Constraints

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