397 citations · 1.8k across the 51 of their papers we have counts for
49 papers · 1 filter
"The Law Doesn't Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners
Nathan Wintersgill, Trevor Stalnaker, Laura A. Heymann +2
Most modern software products incorporate open source components, which requires compliance with each component's licenses. As noncompliance can lead to significant repercussions,…
BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems
Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro +3
Software Bills of Materials (SBOMs) have emerged as tools to facilitate the management of software dependencies, vulnerabilities, licenses, and the supply chain. While significant…
Benchmarking Causal Study to Interpret Large Language Models for Source Code
Daniel Rodriguez-Cardenas, David N. Palacio, Dipin Khati +2
One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. Although…
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
David N Palacio, Alejandro Velasco, Daniel Rodriguez-Cardenas +2
Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. T…
BURT: A Chatbot for Interactive Bug Reporting
Yang Song, Junayed Mahmud, Nadeeshan De Silva +5
This paper introduces BURT, a web-based chatbot for interactive reporting of Android app bugs. BURT is designed to assist Android app end-users in reporting high-quality defect inf…
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…
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation
Kevin Moran, Ali Yachnes, George Purnell +5
Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning pro…
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…
Toward Interactive Bug Reporting for (Android App) End-Users
Yang Song, Junayed Mahmud, Ying Zhou +4
Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing website…
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…
Enhancing Mobile App Bug Reporting via Real-time Understanding of Reproduction Steps
Mattia Fazzini, Kevin Moran, Carlos Bernal Cardenas +3
One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually wri…
Using Pre-Trained Models to Boost Code Review Automation
Rosalia Tufano, Simone Masiero, Antonio Mastropaolo +3
Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility…
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…
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…
SE: Mutation-based Evaluation of Security-focused Static Analysis Tools for Android
Amit Seal Ami, Kaushal Kafle, Kevin Moran +2
This demo paper presents the technical details and usage scenarios of SE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testi…
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…
Towards Automating Code Review Activities
Rosalia Tufano, Luca Pascarella, Michele Tufano +2
Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of int…
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…