106 citations · 136 across the 7 of their papers we have counts for
7 papers
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Cody Watson, Nathan Cooper, David Nader Palacio +2
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). The…
On Learning Meaningful Assert Statements for Unit Test Cases
Cody Watson, Michele Tufano, Kevin Moran +2
Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to…
DeepMutation: A Neural Mutation Tool
Michele Tufano, Jason Kimko, Shiya Wang +4
Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount impo…
On Learning Meaningful Code Changes via Neural Machine Translation
Michele Tufano, Jevgenija Pantiuchina, Cody Watson +2
Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such a…
Learning How to Mutate Source Code from Bug-Fixes
Michele Tufano, Cody Watson, Gabriele Bavota +3
Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are…
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Michele Tufano, Cody Watson, Gabriele Bavota +3
Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to f…