Seq2Code: Transformer-Based Encoder-Decoder Model for Python Source Code Generation

Laskari, Naveen Kumar; Reddy, K. Adi Narayana; Reddy, M. Indrasena

doi:10.1007/978-981-19-9225-4_23

Cited by 1 publication

(1 citation statement)

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“…Laskari et al [79] discussed Seq2Code, a transformer-based solution for translating natural language problem statements into Python source code. Using an encoderdecoder transformer design with multi-head attention and separate embeddings for special characters, the model demonstrated improved perplexity compared to similarly structured models.…”

Section: Code Generation Processmentioning

confidence: 99%

AI-Assisted Programming Tasks Using Code Embeddings and Transformers

Kotsiantis,

Verykios,

Tzagarakis

2024

Electronics

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This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.

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Section: Code Generation Processmentioning

confidence: 99%