# A Comparative Study on Code Generation with Transformers

arXiv: https://arxiv.org/abs/2412.05749 · HTML: https://mlsanity.com/paper/2412.05749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 7 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Text Generation by Learning from Demonstrations](https://mlsanity.com/paper/2009.07839/) (2020)

## Abstract (arXiv)

In an era of widespread influence of Natural Language Processing (NLP), there have been multiple research efforts to supplant traditional manual coding techniques with automated systems capable of generating solutions autonomously. With rapid research for code generation and a sole focus on large language models, there emerges a need to compare and evaluate the performance of transformer architectures based on several complexities of the model. This paper introduces the concept of a "A Comparative Study on Code Generation with Transformers," a model based on Transformer architecture, and NLP methodologies to automatically generate C++ source code for different varieties of problems. Here, a comparative study is performed to evaluate the robustness of transformer-based models on the basis of their architecture complexities and their capability to handle diverse problem sets, from basic arithmetic to complex computations.