416 citations · 493 across the 12 of their papers we have counts for
6 papers · 1 filter
Execution-based Evaluation for Data Science Code Generation Models
Junjie Huang, Chenglong Wang, Jipeng Zhang +6
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress…
Exploring and Evaluating Personalized Models for Code Generation
Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy +3
Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling so…
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance
Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement +2
Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched…
Generating Examples From CLI Usage: Can Transformers Help?
Roshanak Zilouchian Moghaddam, Spandan Garg, Colin B. Clement +2
Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks. Relying on outdated documenta…
Learning to Reduce False Positives in Analytic Bug Detectors
Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin +4
Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers re…
Training and Evaluating a Jupyter Notebook Data Science Assistant
Shubham Chandel, Colin B. Clement, Guillermo Serrato +1
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…