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Found 5 papers, 1 papers with code
A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models
We investigate whether this correction capability of Large Language Models (LLMs) extends to Automatic Program Repair (APR).
An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair.
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
These findings show that early layers can be used to obtain better results using the same resources, as well as to reduce resource usage during fine-tuning and inference.
Fully Autonomous Programming with Large Language Models
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to small imperfections, such as the wrong input or output format.
Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods
The expected results of this work are improved code representations for automatic program repair and, specifically, fixing security vulnerabilities.
