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Found 13 papers, 7 papers with code
An Exploratory Study of ML Sketches and Visual Code Assistants
We investigate developers' mental models by analyzing patterns commonly observed in their sketches when developing an ML workflow.
Learning Defect Prediction from Unrealistic Data
To investigate this conjecture, we propose an approach for identifying the subsets of these large yet unrealistic datasets that are most similar to examples in real-world datasets based on their learned representations.
Large Language Models for Test-Free Fault Localization
Specifically, we propose to overcome the left-to-right nature of LLMs by fine-tuning a small set of bidirectional adapter layers on top of the representations learned by LLMs to produce LLMAO, the first language model based fault localization approach that locates buggy lines of code without any test coverage information.
CAT-LM: Training Language Models on Aligned Code And Tests
We also drastically increase the maximum sequence length of inputs to 8, 192 tokens, 4x more than typical code generation models, to ensure that the code context is available to the model when generating test code.
Skill over Scale: The Case for Medium, Domain-Specific Models for SE
We align established best-practices for pre-training large language models with properties of SO as a data source, especially using a large context window (2, 048 tokens), coupled with a powerful toolkit (Megatron-LM) to train two models: SOBertBase (125M parameters) and SOBertLarge (762M parameters), at a budget of just $374 and $1600 each.
AI for Low-Code for AI
We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks.
DiffusER: Discrete Diffusion via Edit-based Reconstruction
In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm.
A Systematic Evaluation of Large Language Models of Code
We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages.
Capturing Structural Locality in Non-parametric Language Models
Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies.
Memorization and Generalization in Neural Code Intelligence Models
The goal of this paper is to evaluate and compare the extent of memorization and generalization in neural code intelligence models.
Understanding Neural Code Intelligence Through Program Simplification
Our approach, SIVAND, uses simplification techniques that reduce the size of input programs of a CI model while preserving the predictions of the model.
Patching as Translation: the Data and the Metaphor
Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions.
Global Relational Models of Source Code
By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.
