activity
20172026
most citedOn Learning Meaningful Assert Statements for Unit Test Cases

106 citations · 323 across the 41 of their papers we have counts for

collaborators
Showing 2022Show all

11 papers · 1 filter

cs.SE2022

Automated Variable Renaming: Are We There Yet?

Antonio Mastropaolo, Emad Aghajani, Luca Pascarella +1

Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support deve…

cs.SE2022

Don't Reinvent the Wheel: Towards Automatic Replacement of Custom Implementations with APIs

Rosalia Tufano, Emad Aghajani, Gabriele Bavota

Reusing code is a common practice in software development: It helps developers speedup the implementation task while also reducing the chances of introducing bugs, given the assump…

cs.SE2022★ 2 cited

Detecting Connectivity Issues in Android Apps

Alejandro Mazuera-Rozo, Camilo Escobar-Velásquez, Juan Espitia-Acero +2

Android is the most popular mobile operating system in the world, running on more than 70% of mobile devices. This implies a gigantic and very competitive market for Android apps.…

cs.SE2022★ 5 cited

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…

cs.SE2022★ 61 cited

AI-driven Development Is Here: Should You Worry?

Neil Ernst, Gabriele Bavota

AI-Driven Development Environments (AIDEs) Integrate the power of modern AI into IDEs like Visual Studio Code and JetBrains IntelliJ. By leveraging massive language models and the…

cs.SE2022

To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?

Matteo Ciniselli, Luca Pascarella, Gabriele Bavota

Deep Learning (DL) models have been widely used to support code completion. These models, once properly trained, can take as input an incomplete code component (e.g., an incomplete…