Large language models for software engineering: A systematic literature review
Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …
Software Engineering (SE). Many recent publications have explored LLMs applied to …
A survey of machine learning for big code and naturalness
Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …
engineering has recently taken important steps in proposing learnable probabilistic models …
Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Program synthesis or code generation aims to generate a program that satisfies a problem
specification. Recent approaches using large-scale pretrained language models (LMs) have …
specification. Recent approaches using large-scale pretrained language models (LMs) have …
A systematic evaluation of large language models of code
Large language models (LMs) of code have recently shown tremendous promise in
completing code and synthesizing code from natural language descriptions. However, the …
completing code and synthesizing code from natural language descriptions. However, the …
Competition-level code generation with alphacode
Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist
programmers or even generate programs themselves could make programming more …
programmers or even generate programs themselves could make programming more …
Program synthesis with large language models
This paper explores the limits of the current generation of large language models for
program synthesis in general purpose programming languages. We evaluate a collection of …
program synthesis in general purpose programming languages. We evaluate a collection of …
Self-planning code generation with large language models
Although large language models (LLMs) have demonstrated impressive ability in code
generation, they are still struggling to address the complicated intent provided by humans. It …
generation, they are still struggling to address the complicated intent provided by humans. It …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Codexglue: A machine learning benchmark dataset for code understanding and generation
Benchmark datasets have a significant impact on accelerating research in programming
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …
Jigsaw: Large language models meet program synthesis
Large pre-trained language models such as GPT-3 [10], Codex [11], and Google's language
model [7] are now capable of generating code from natural language specifications of …
model [7] are now capable of generating code from natural language specifications of …