Search Results for author: Nathan Cooper

Found 7 papers, 1 papers with code

Stable Code Technical Report

no code implementations1 Apr 2024 Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, Jonathan Tow, James Baicoianu, Ashish Datta, Maksym Zhuravinskyi, Dakota Mahan, Marco Bellagente, Carlos Riquelme, Nathan Cooper

Stable Code Instruct also exhibits state-of-the-art performance on the MT-Bench coding tasks and on Multi-PL completion compared to other instruction tuned models.

Code Completion Language Modelling +2

Intelligent Software Tooling for Improving Software Development

no code implementations17 Oct 2023 Nathan Cooper

Software has eaten the world with many of the necessities and quality of life services people use requiring software.

Toward a Theory of Causation for Interpreting Neural Code Models

no code implementations7 Feb 2023 David N. Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk

To demonstrate the practical benefit of $do_{code}$, we illustrate the insights that our framework can provide by performing a case study on two popular deep learning architectures and ten NCMs.

Causal Inference

It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

1 code implementation22 Jan 2021 Nathan Cooper, Carlos Bernal-Cárdenas, Oscar Chaparro, Kevin Moran, Denys Poshyvanyk

Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers.

Optical Character Recognition Optical Character Recognition (OCR) +2

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

no code implementations14 Sep 2020 Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Denys Poshyvanyk

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL).

Automated Feature Engineering Feature Engineering

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

no code implementations18 May 2020 Carlos Bernal-Cárdenas, Nathan Cooper, Kevin Moran, Oscar Chaparro, Andrian Marcus, Denys Poshyvanyk

In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers.

Image Classification object-detection +1

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