98 citations · 201 across the 13 of their papers we have counts for
14 papers
Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs
Yanfu Yan, Nathan Cooper, Kevin Moran +3
Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding…
On the Generalizability of Transformer Models to Code Completions of Different Lengths
Nathan Cooper, Rosalia Tufano, Gabriele Bavota +1
The programming landscape is nowadays being reshaped by the advent of Large Language Models (LLMs) able to automate code-related tasks related to code implementation (e.g., code co…
Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
Yanfu Yan, Nathan Cooper, Oscar Chaparro +2
Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI). However, developing automated techniques to mana…
MASC: A Tool for Mutation-Based Evaluation of Static Crypto-API Misuse Detectors
Amit Seal Ami, Syed Yusuf Ahmed, Radowan Mahmud Redoy +5
While software engineers are optimistically adopting crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a…
Toward a Theory of Causation for Interpreting Neural Code Models
David N. Palacio, Alejandro Velasco, Nathan Cooper +3
Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilitie…
Translating Video Recordings of Complex Mobile App UI Gestures into Replayable Scenarios
Carlos Bernal-Cárdenas, Nathan Cooper, Madeleine Havranek +4
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…
Using Transfer Learning for Code-Related Tasks
Antonio Mastropaolo, Nathan Cooper, David Nader Palacio +4
Deep learning (DL) techniques have been used to support several code-related tasks such as code summarization and bug-fixing. In particular, pre-trained transformer models are on t…
An Empirical Study on the Usage of Transformer Models for Code Completion
Matteo Ciniselli, Nathan Cooper, Luca Pascarella +5
Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the…
Mutation-based Evaluation of Cryptographic API Misuse Detectors
Amit Seal Ami, Scott Marsden, Kevin Moran +2
The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed fo…
An Empirical Study on the Usage of BERT Models for Code Completion
Matteo Ciniselli, Nathan Cooper, Luca Pascarella +3
Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the…
V2S: A Tool for Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Madeleine Havranek, Carlos Bernal-Cárdenas, Nathan Cooper +3
Screen recordings are becoming increasingly important as rich software artifacts that inform mobile application development processes. However, the amount of manual effort required…
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper +4
Deep learning (DL) techniques are gaining more and more attention in the software engineering community. They have been used to support several code-related tasks, such as automati…
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…