Efficient Enumeration of Regular Expressions for Faster Regular Expression Synthesis

Kim, Su-Hyeon; Im, Hyeonseung; Ko, Sang‐Ki

doi:10.1007/978-3-030-79121-6_6

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The main computational bottleneck comes from the repetitive regex equivalence tests as there are too many regexes in the set. In order to reduce the size of the constructed set, we employ the concise normal form [11] of regexes which are proven to be useful to sufficiently reduce the number of redundant regexes. For instance, we inductively apply substitution rules for subregexes such as…”

Section: Heuristics For Faster Computationmentioning

confidence: 99%

“…If a student submits (aaa + b * ) * + b * as a solution, then the system should give the full grade since the submitted regex recognizes the desired regular language. While assigning a full grade to the submission, our algorithm provides (aaa + b) * to the student by computing the concise normal form [11] of the submission so that the student can recognize that there is a better solution (in terms of syntactic conciseness).…”

Section: Converting Logic Formulas To Nl Descriptionsmentioning

confidence: 99%

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Automated Grading of Regular Expressions

Kim

Han

et al. 2023

Lecture Notes in Computer Science

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With the rapid transition to distance learning, automatic grading software becomes more important to both teachers and students. We study the problem of automatically grading the regular expressions submitted by students in courses related to automata and formal language theory. In order to utilize the semantic information of the regular expression, we define a declarative logic that can be described by regular language and at the same time has natural language characteristics, and use it for the following tasks: 1) to assign partial grades for incorrect regular expressions and 2) to provide helpful feedback to students to make them understand the reason for the grades and a way to revise the incorrect regular expressions into correct ones. We categorize the cases when students’ incorrect submissions deserve partial grades and suggest how to assign appropriate grades for each of the cases. In order to optimize the runtime complexity of the algorithm, two heuristics based on automata theory are proposed and evaluated on the dataset collected from undergraduate students. In addition, we suggest Regex2NL which translates regular expressions to natural language descriptions to give insight to students so that they can understand how the regular expressions work.

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Section: Heuristics For Faster Computationmentioning

confidence: 99%

Section: Converting Logic Formulas To Nl Descriptionsmentioning

confidence: 99%

Automated Grading of Regular Expressions

Kim

Han

et al. 2023

Lecture Notes in Computer Science

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“…• AlphaRegex [Lee et al, 2016, Kim et al, 2021: a best-first search based regex enumeration algorithm. We use our own Python implementation of AlphaRegex algorithm, which is originally written in OCaml 4 together with the recent search space reduction techniques by Kim et al [2021].…”

Section: Architecture Of the Neuralsplittermentioning

confidence: 99%

Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting

Kim¹,

Cheon²,

Han³

et al. 2022

Preprint

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Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. We tackle the problem of learning regexes faster from positive and negative strings by relying on a novel approach called 'neural example splitting'. Our approach essentially split up each example string into multiple parts using a neural network trained to group similar substrings from positive strings. This helps to learn a regex faster and, thus, more accurately since we now learn from several short-length strings. We propose an effective regex synthesis framework called 'SplitRegex' that synthesizes subregexes from 'split' positive substrings and produces the final regex by concatenating the synthesized subregexes. For the negative sample, we exploit pre-generated subregexes during the subregex synthesis process and perform the matching against negative strings. Then the final regex becomes consistent with all negative strings. SplitRegex is a divided-and-conquer framework for learning target regexes; split (=divide) positive strings and infer partial regexes for multiple parts, which is much more accurate than the whole string inferring, and concatenate (=conquer) inferred regexes while satisfying negative strings. We empirically demonstrate that the proposed SplitRegex framework substantially improves the previous regex synthesis approaches over four benchmark datasets.Preprint. Under review.

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Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions

Tang,

Yan,

et al. 2024

Lecture Notes in Computer Science

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Efficient Enumeration of Regular Expressions for Faster Regular Expression Synthesis

Cited by 5 publications

References 14 publications

Automated Grading of Regular Expressions

Automated Grading of Regular Expressions

Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting

Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions

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