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Found 8 papers, 2 papers with code
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance
Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations.
Generating Examples From CLI Usage: Can Transformers Help?
Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks.
Training and Evaluating a Jupyter Notebook Data Science Assistant
We study the feasibility of a Data Science assistant powered by a sequence-to-sequence transformer by training a new model JuPyT5 on all publicly available Jupyter Notebook GitHub repositories and developing a new metric: Data Science Problems (DSP).
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.
Distilling Transformers for Neural Cross-Domain Search
Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks.
DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons
The joint task of bug localization and program repair is an integral part of the software development process.
PyMT5: multi-mode translation of natural language and Python code with transformers
Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding.
On the Use of ArXiv as a Dataset
We use this pipeline to extract and analyze a 6. 7 million edge citation graph, with an 11 billion word corpus of full-text research articles.
