Sequence-to-Sequence Learning Based Conversion of Pseudo-code to Source code Using Neural Translation Approach
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TL;DR: In this paper , the authors proposed 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.
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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.
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Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language
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AI-Assisted Programming Tasks Using Code Embeddings and Transformers
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TL;DR: The role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks is discussed, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.
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Balancing quality and efficiency: An improved non-autoregressive model for pseudocode-to-code conversion
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Additional file 1 of Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language
30 Nov 2023
Abstract: Additional file 1. Some parts of the codes.
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