Search Results for author:
Found 8 papers, 3 papers with code
Repoformer: Selective Retrieval for Repository-Level Code Completion
Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion.
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
A Static Evaluation of Code Completion by Large Language Models
Large language models trained on code have shown great potential to increase productivity of software developers.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i. e., in-file context, but ignore the rich semantics in other files within the same project, i. e., cross-file context, a critical source of information that is especially useful in modern modular software development.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
