String Generation for Testing Regular Expressions

Zheng, Lixiao; Ma, Shuai; Wang, Yuanyang; Lin, Gang

doi:10.1093/comjnl/bxy137

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…For example, W3C specification requires that the content models of XML schema language DTDs and XSDs [17] must be deterministic regular expressions, while there are no determinism restric-tions on Relax NG [18] and regular expressions used for string pattern matching. The work in [32] is devoted to finding how deterministic real regular expressions are. Li et al [32] found that more than 98% of regular expressions in Relax NG are deterministic, although Relax NG does not have the determinism constraint for its content models.…”

Section: Determinismmentioning

confidence: 99%

“…The work in [32] is devoted to finding how deterministic real regular expressions are. Li et al [32] found that more than 98% of regular expressions in Relax NG are deterministic, although Relax NG does not have the determinism constraint for its content models. Besides, more than half of regular expressions from RegExLib are deterministic.…”

Section: Determinismmentioning

confidence: 99%

“…To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Testing status [14] RE coverage based [33] From positive examples [52] Syntax check [10] Highlight [72−74] Based on positive examples [83] Bug classification [22] DFA coverage based [11] From both examples [53−57] Semantic check [68−70] Automaton graph [75−79] Based on negative examples [84] Feature usage [8] Mutation based [49] Gold′s learning [59−61] Verification [71] Tree view [74,80] Based on both examples [85] Evolution [24] Sampling [40,41] From specifications [62−64] Visual IDE [81] Based on genetic programming [88,89] Comprehension [23] Enumerating [91] From specifications and examples [66] Visual structure [82] Interactive repair [90] Composition [25] Random [45,47] Abstraction [12] Repair vulnerable [86,87] Portability [28] Systematic [46−48] Development cycle [30] Determinism [32] Readability [92]…”

Section: Open Accessmentioning

confidence: 99%

See 2 more Smart Citations

Ensuring the Correctness of Regular Expressions: A Review

Zheng

Chen

et al. 2021

Int. J. Autom. Comput.

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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.

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Section: Determinismmentioning

confidence: 99%

Section: Determinismmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

See 1 more Smart Citation

Ensuring the Correctness of Regular Expressions: A Review

Zheng

Chen

et al. 2021

Int. J. Autom. Comput.

Self Cite

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“…In creating a first-time pain data collaboration framework explicitly developed for pain studies, realtime data collection, multiple study cooperation on the very same data and/or respondents, the system leads to the development of an enhanced automated stress detection tool that produces impressive outcomes. Zheng et al, in (2020) [20], proposed coverage criteria-based string generation for checking regular expressions, Second, the method introduces a definition of the criterion of pair coverage for regular expressions and analyses the relationships of sub assumption with current criteria of coverage for both regular grammars and finite automata. Second, machine design an algorithm that outputs a small set of strings that satisfy the criterion of pair coverage as an input of a regular expression.…”

Section: Review Of Literaturementioning

confidence: 99%

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Puri¹,

Patnaik²

2021

INDJCSE

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Machine learning supervised classification plays major role in large text classification. In health care data it contributes since couple of years for generating the privacy and security. Such kind of electric records might take large data on storage devices, so it needs to optimize with some processing techniques. The Natural Language Processing (NLP) features to represent text as a vector appropriate for use in machine learning algorithms. To describe text as just a vector suitable for use in machine learning algorithms, text classification techniques frequently rely on dictionary counters including bag-of-words as well NLP techniques. As an alternative feature set, editable search queries are proposed in this work. The suggested model utilizes the Smith Waterman (SW) optimization created by Pair -wise alignment to create a set of pattern matching characteristics based on labeled textual information or to train a later part classifier. The Naive Bayes (NB) supervised learning algorithm has used for classification. While a correlation of the special groups and traditional text characteristics shows that a classification algorithm does not make better decisions utilizing generated features, the produced factors can catch patterns that cannot be identified with collective expertise. As a result, it is significantly boost a classifier that combines conventional methods for generated characteristics. In the experimental analysis we evaluate the proposed system results with various existing systems that our system demonstrates effective results than traditional approaches.

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“…Conventional rule-based [10,11] log parsers require professionals to manually create massive complex regular expressions (each regular expression [12] corresponds to a log template) and add them to the parsing rule set. In the process of log parsing, log messages are matched with the regular expressions in the rule set one by one.…”

Section: Introductionmentioning

confidence: 99%

Spray: Streaming Log Parser for Real-Time Analysis

Zou

Chen

Luo

et al. 2022

Security and Communication Networks

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Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.

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String Generation for Testing Regular Expressions

Cited by 3 publications

References 26 publications

Ensuring the Correctness of Regular Expressions: A Review

Ensuring the Correctness of Regular Expressions: A Review

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Spray: Streaming Log Parser for Real-Time Analysis

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