Neural Network Approach to Program Synthesis for Tabular Transformation by Example

Data transformation is a laborious and time-consuming task for analysts. Programming by example (PBE) is a technique that can simplify this difficult task for data analysts by automatically generating programs for data transformation. Most of the previously proposed PBE methods are based on search a...

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Bibliographic Details
Published in:IEEE Access Vol. 10; pp. 24864 - 24876
Main Authors: Ujibashi, Yoshifumi, Takasu, Atsuhiro
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automatic programming
beam search
Coders
Data analysis
Data integration
Data models
Decoding
Domain specific languages
Electrical engineering. Electronics. Nuclear engineering
Encoding
LSTM
Machine learning
Machine learning algorithms
Memory
neural network
Neural networks
program synthesis
programming by example
Search algorithms
Strings
Tables (data)
tabular data
TK1-9971
Training data
Transformations
transformer
Transformers
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Data transformation is a laborious and time-consuming task for analysts. Programming by example (PBE) is a technique that can simplify this difficult task for data analysts by automatically generating programs for data transformation. Most of the previously proposed PBE methods are based on search algorithms, but recent improvements in machine learning (ML) have led to its application in PBE research. For example, RobustFill was proposed as an ML-based PBE method for string transformation by using long short-term memory (LSTM) as the sequential encoder-decoder model. However, an ML-based PBE method has not been developed for tabular transformations, which are used frequently in data analysis. Thus, in the present study, we propose an ML-based PBE method for tabular transformations. First, we consider the features of tabular transformations, which are more complex and data intensive than string transformations, and propose a new ML-based PBE method using the state-of-the-art Transformer sequential encoder-decoder model. To our knowledge, this is the first ML-based PBE method for tabular transformations. We also propose two decoding methods comprising multistep beam search and program validation-beam search, which are optimized for program generation, and thus generate correct programs with higher accuracy. Our evaluation results demonstrated that the Transformer-based PBE model performed much better than LSTM-based PBE when applied to tabular transformations. Furthermore, the Transformer-based model with the proposed decoding method performed better than the conventional PBE model using the search-based method.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3155468