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Found 5 papers, 3 papers with code
INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers
We find that models that incorporate some structural information (such as GraphCodeBERT) have a better representation of source code characteristics.
JEMMA: An Extensible Java Dataset for ML4Code Applications
To demonstrate the utility of the dataset, we also report results from two empirical studies on our data, ultimately showing that significant work lies ahead in the design of context-aware source code models that can reason over a broader network of source code entities in a software project, the very task that JEMMA is designed to help with.
Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation
In this work, we evaluate the code synthesis capabilities of the Codex model based on a set of 115 Python problem statements from a popular competitive programming portal: HackerRank.
What do pre-trained code models know about code?
Pre-trained models of code built on the transformer architecture have performed well on software engineering (SE) tasks such as predictive code generation, code summarization, among others.
GLUECode: A Benchmark for Source Code Machine Learning Models
To address this, we present GLUECode, Global and Local Understanding Evaluation of Code, a benchmark of diverse tasks to evaluate machine learning models of source code.
