| Autor: |
Quansheng DOU, Shun ZHANG, Hao PAN, Huixian WANG, Huanling TANG |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Tongxin xuebao, Vol 44, Pp 249-259 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2023203 |
| Popis: |
In order to address the issue of rapid growth of program space in SQUARES, which led to low efficiency in program synthesis, a program space reducer based on deep neural network (DNN) was introduced into the SQUARES framework.A given ＆lt;Queried tables, Query result＆gt; pair was represented as a 2D tensor which was used as input for a DNN.And the output of the DNN was the relevance vector of the target SQL statement synthesis rules.Based on the output of the DNN, the last N rules with weak correlation to the target SQL statement were eliminated, thereby shrinking the program search space and improving the system synthesis efficiency.The architecture of DNN, the method of generating training datasets, and the training process of DNN were described in detail.Furthermore, experimental comparisons between PSR-SQUARES and other representative SQL reverse synthesis systems were conducted.The results show that the overall performance of PSR-SQUARES is superior to other synthesis systems to varying degrees, with the average synthesis time reduced from 251 s in SQUARES to 130 s and the target program synthesis success rate increased from 80% to 89%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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