Practical Study of Deterministic Regular Expressions from Large-scale XML and Schema Data

et al. 2021

Int. J. Autom. Comput.

Regular expressions are widely used within and even outside of computer science due to their expressiveness and flexibility. However, regular expressions have a quite compact and rather tolerant syntax that makes them hard to understand, hard to compose, and error-prone. Faulty regular expressions may cause failures of the applications that use them. Therefore, ensuring the correctness of regular expressions is a vital prerequisite for their use in practical applications. The importance and necessity of ensuring correct definitions of regular expressions have attracted extensive attention from researchers and practitioners, especially in recent years. In this study, we provide a review of the recent works for ensuring the correct usage of regular expressions. We classify those works into different categories, including the empirical study, test string generation, automatic synthesis and learning, static checking and verification, visual representation and explanation, and repairing. For each category, we review the main results, compare different approaches, and discuss their advantages and disadvantages. We also discuss some potential future research directions.

Section: Determinismmentioning

confidence: 99%

Ensuring the Correctness of Regular Expressions: A Review

Zheng

et al. 2021

Int. J. Autom. Comput.

“…14: The proportion of subclasses on Relax NG. The dataset used for this statistical experiment is acquired from [28], with 509, 267 regular expressions from 4, 526 Rleax NG schemas.…”

Section: Usage Of Soire In Practicementioning

confidence: 99%

“…Most existing subclasses of regular expressions for XML are defined on standard regular expressions, e.g., [5,7,6,16,35] which were analyzed together in [31,28]. For single occurrence regular expressions (SOREs), in which each symbol occurs at most once and its subclass chain regular expressions (CHAREs), Bex et al proposed two inference algorithms RWR and CRX [7,8].…”

Section: Introductionmentioning

confidence: 99%

An effective algorithm for learning single occurrence regular expressions with interleaving

Proceedings of the 23rd International Database Applications &Amp; Engineering Symposium on - IDEAS '19

Zhang

et al. 2019

Self Cite

The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.

“…Such kinds of applications motivate us to investigate the problem of learning RE(&) from positive and negative examples. Most researchers have studied subclasses of REs, which are expressive enough to cover the vast majority of real-world applications [6,7,22] and perform better on several decision problems than general ones [6,7,19,20,25,27]. Bex et al [3] proposed learning algorithms for two subclasses of REs: SOREs and CHAREs, which capture many practical DTDs/XSDs and are both single occurrence REs.…”

Section: Introductionmentioning

confidence: 99%

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Advances in Knowledge Discovery and Data Mining

Zhang

et al. 2020

Self Cite

The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a schema or a valid schema. Therefore, it is essential to devise algorithms to infer schemas. The fundamental task in XML schema inference is to learn regular expressions. In this paper, we focus on learning the subclass of RE(&) called SIREs (the subclass of regular expressions with interleaving). Previous work in this direction lacks inference algorithms that support inference from positive and negative examples. We provide an algorithm to learn SIREs from positive and negative examples based on genetic algorithms and parallel techniques. Our algorithm also has better expansibility, which means that our algorithm not only supports learning with positive and negative examples, but also supports learning with positive or negative examples only. Experimental results demonstrate the effectiveness of our algorithm.