397 citations · 1.8k across the 51 of their papers we have counts for
6 papers · 1 filter
Deep Learning & Software Engineering: State of Research and Future Directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum +6
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was co…
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…
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Carlos Bernal-Cárdenas, Nathan Cooper, Kevin Moran +3
Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a pop…
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas +4
Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually esta…
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…