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

no code implementations • 14 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

DeepMutation: A Neural Mutation Tool

no code implementations • 12 Feb 2020 • Michele Tufano, Jason Kimko, Shiya Wang, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Denys Poshyvanyk

To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants.

Decoder Fault Detection

On Learning Meaningful Code Changes via Neural Machine Translation

no code implementations • 25 Jan 2019 • Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, Denys Poshyvanyk

We show that, when applied in a narrow enough context (i. e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases.

Bug fixing Machine Translation +2

Learning How to Mutate Source Code from Bug-Fixes

no code implementations • 27 Dec 2018 • Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk

Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model).

Software Engineering