Squares

Orvalho, Pedro; Terra-Neves, Miguel; Ventura, Miguel; Martins, Ruben; Manquinho, Vasco

doi:10.14778/3415478.3415492

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Enumeration-based program synthesis is a common approach to program synthesis, where the core idea is to perform an enumerative search in the space of programs that satisfy constraints. Many synthesizers employ enumeration-based synthesis, such as Morpheus [20], Neo [22], Trinity [23], SQUARE [19], and Cubes [12]. Enumerationbased program synthesis typically relies on logical derivation and requires the prior use of Boolean satisfiability (SAT) or satisfiability model theory (SMT) encodings to represent the space of feasible programs.…”

Section: Enumeration-based Program Synthesismentioning

confidence: 99%

“…The essence of Query Reverse Engineering is to generate a SQL statement by leveraging the provided database and the desired output table. The most recent models that have emerged in this area include SQLSynthesizer [7],OLAP [9],Scythe [8] ,SQuID [10], PATSQL [11], Cubes [12] and SQUARE [19]. Various methodologies exist for Query Reverse Engineering, including the utilization of decision trees with predefined templates [6,7], abstract representations of queries that satisfy input-output examples [8], and Enumeration-Based Program Synthesis [12,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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LGEQRE: Learning Guided Enumerative Synthesis for Query Reverse Engineering

Wang,

Dou,

Tang

et al. 2023

Preprint

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To address the problem of users' lack of SQL query writing skills, Query Reverse Engineering (QRE) was proposed, where the goal of QRE is to generate a SQL statement based on a given database and query output table. SQUARES is one of the state-of-the-art models in the field, which enumerates constraint-compliant programs using a solver-based enumerator, and since the Solver randomly enumerates candidate programs, SQUARES synthesis is not very efficient. In this paper, we propose LGEQRE based on SQUARES, a learning-based approach to guide the enumeration of candidate programs. LGEQRE predicts the operators be required by neural network, sorts and deletes operators based on the prediction, and uses an Optimizer-based enumerator to enumerate programs according to the predicted probability of the operators. Under the same experimental conditions, the experimental results showed that LGEQRE increased the synthesis rate from 80% to 89.1% and reduced the average synthesis time from 251s to 117s compared to SQUARES.

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Section: Enumeration-based Program Synthesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LGEQRE: Learning Guided Enumerative Synthesis for Query Reverse Engineering

Wang,

Dou,

Tang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The latter is a state of the art encoding for the synthesis of SQL queries [17]. k-tree is the default enumerator in Trinity [13], and the line-based enumerator is available in Squares [16]. The k-tree encoding has a very similar structure to that of multi-tree, so our pruning techniques were easily applied to this encoding.…”

Section: Multi-tree Versus K-tree and Line-based Encodingsmentioning

confidence: 99%

FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

Ferreira

Terra-Neves²,

Ventura³

et al. 2021

Tools and Algorithms for the Construction and Analysis of Systems

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Form validators based on regular expressions are often used on digital forms to prevent users from inserting data in the wrong format. However, writing these validators can pose a challenge to some users.We present Forest, a regular expression synthesizer for digital form validations. Forest produces a regular expression that matches the desired pattern for the input values and a set of conditions over capturing groups that ensure the validity of integer values in the input. Our synthesis procedure is based on enumerative search and uses a Satisfiability Modulo Theories (SMT) solver to explore and prune the search space. We propose a novel representation for regular expressions synthesis, multi-tree, which induces patterns in the examples and uses them to split the problem through a divide-and-conquer approach. We also present a new SMT encoding to synthesize capture conditions for a given regular expression. To increase confidence in the synthesized regular expression, we implement user interaction based on distinguishing inputs.We evaluated Forest on real-world form-validation instances using regular expressions. Experimental results show that Forest successfully returns the desired regular expression in 70% of the instances and outperforms Regel, a state-of-the-art regular expression synthesizer.

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“…The latter is a state of the art encoding for the synthesis of SQL queries [19]. k-tree is implemented as the default enumerator in Trinity [15], while the line-based enumerator is used as available in Squares [18]. The k-tree encoding has a very similar structure to that of multi-tree, so our pruning techniques were easily applied to this encoding.…”

Section: Multi-tree Versus K-tree and Line-based Encodingsmentioning

confidence: 99%

FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

Ferreira¹,

Terra-Neves²,

Ventura³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Form validators based on regular expressions are often used on digital forms to prevent users from inserting data in the wrong format. However, writing these validators can pose a challenge to some users. We present Forest, a regular expression synthesizer for digital form validations. Forest produces a regular expression that matches the desired pattern for the input values and a set of conditions over capturing groups that ensure the validity of integer values in the input. Our synthesis procedure is based on enumerative search and uses a Satisfiability Modulo Theories (SMT) solver to explore and prune the search space. We propose a novel representation for regular expressions synthesis, multitree, which induces patterns in the examples and uses them to split the problem through a divide-and-conquer approach. We also present a new SMT encoding to synthesize capture conditions for a given regular expression. To increase confidence in the synthesized regular expression, we implement user interaction based on distinguishing inputs. We evaluated Forest on real-world form-validation instances using regular expressions. Experimental results show that Forest successfully returns the desired regular expression in 72% of the instances and outperforms Regel, a state-of-the-art regular expression synthesizer.

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Squares

Cited by 12 publications

References 15 publications

LGEQRE: Learning Guided Enumerative Synthesis for Query Reverse Engineering

LGEQRE: Learning Guided Enumerative Synthesis for Query Reverse Engineering

FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

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