Search Results for author:
Found 9 papers, 4 papers with code
Insights from the Usage of the Ansible Lightspeed Code Completion Service
The code for Ansible Lightspeed service and the analysis framework is made available for others to use.
Learning Transfers over Several Programming Languages
Third, which characteristics of a language pair are predictive of transfer performance, and how does that depend on the given task.
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and conventional accuracy of a model at the same time.
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
This work focuses on the generation of Ansible-YAML, a widely used markup language for IT Automation.
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts
We pre-train our model with a much smaller dataset, the size of which is only 5% of the state-of-the-art models' training datasets, to illustrate the effectiveness of our data augmentation and the pre-training approach.
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
However, existing datasets to train models for vulnerability identification suffer from multiple limitations such as limited bug context, limited size, and synthetic and unrealistic source code.
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
