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Found 10 papers, 6 papers with code
Out of Context: How important is Local Context in Neural Program Repair?
Our results indicate that overall repair success increases with the size of the local context (albeit not for all bug types) and confirm the common practice that roughly 50-60% of the input window should be used for context leading the bug.
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.
RunBugRun -- An Executable Dataset for Automated Program Repair
With this dataset we follow several goals: we want to lift Neural Program Repair beyond fully static code representations, foster the use of execution-based features and, by including several different languages, counterbalance the predominance of Java in the current landscape of APR datasets and benchmarks.
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.
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code
Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code suggestion, improving readability, and API migration.
Software Engineering
Modeling Vocabulary for Big Code Machine Learning
We show that a subset of decisions have decisive characteristics, allowing to train accurate Neural Language Models quickly on a large corpus of 10, 106 projects.
