- Title
- Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach
- Creator
- Acharjee, Uzzal; Arefin, Minhazul; Hossen, Kazi; Uddin, Mohammed; Uddin, Md Ashraf; Islam, Linta
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188479
- Identifier
- vital:17254
- Identifier
-
https://doi.org/10.1109/ACCESS.2022.3155558
- Identifier
- ISSN:2169-3536 (ISSN)
- Abstract
- Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 10, no. (2022), p. 26730-26742
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Machine translation; Natural language processing; Pseudo-code; Sequence-to-sequence learning model; Source code
- Full Text
- Reviewed
- Funder
- This work was supported by the Jagannath University, Dhaka, Bangladesh.
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