FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

Ferreira, Margarida A.; Terra-Neves, Miguel; Ventura, Miguel; Lynce, Inês; Martins, Ruben

doi:10.26226/morressier.604907f41a80aac83ca25cef

Cited by 1 publication

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mayer et al [16] describe two types of user interaction for program disambiguation: in the first approach, users select the correct program among a set of returned solutions which are presented in a way that allows easy navigation. The second approach is described as conversational clarification, where the system iteratively asks questions to the user, further refining the original specification until just one candidate program is left [7,12,14,17,22,31]. In Cubes, we use conversational clarification to improve the confidence in produced solutions while still keeping the complexity for the user low.…”

Section: Related Workmentioning

confidence: 99%

CUBES: A Parallel Synthesizer for SQL Using Examples

Brancas¹,

Terra-Neves²,

Ventura³

et al. 2022

Preprint

View full text Add to dashboard Cite

In recent years, more and more people see their work depend on data manipulation tasks. However, many of these users do not have the background in programming required to write complex programs, particularly SQL queries. One way of helping these users is to automatically synthesize the SQL query given a small set of examples provided by the user -a task known as Query Reverse Engineering. In the last decade, a large plethora of program synthesizers for SQL have been proposed, but none of the current tools take advantage of the increased number of cores per processor.This paper proposes Cubes, a parallel program synthesizer for the domain of SQL queries using input-output examples. Cubes extends current sequential query synthesizers with new pruning techniques and a divide-and-conquer approach, splitting the search space into smaller independent sub-problems.Examples are an under-specification, and the synthesized query may not match the user's intent. We improve the accuracy of Cubes by developing a disambiguation procedure based on fuzzing that interacts with the user and increases our confidence that the returned query matches the user intent.We perform an extensive evaluation on around 4000 SQL queries from different domains. Experimental results show that our sequential version can solve more instances than other state-of-the-art SQL synthesizers. Moreover, the parallel approach can scale up to 16 processes with super-linear speedups for many hard instances. Our disambiguation approach is critical to achieving an accuracy of around 75%, significantly larger than other SQL synthesizers.

show abstract