98 citations · 160 across the 15 of their papers we have counts for
14 papers · 1 filter
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Saima Afrin, Alessandro Midolo, Camilo Escobar-Velásquez +5
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavi…
Writing Bug Reports for Software Repair Agents: What Information Matters Most?
Vincenzo Luigi Bruno, Alessandro Giagnorio, Daniele Bifolco +3
Software development is increasingly moving toward agentic-first workflows. This includes AI agents responsible for generating initial fixes for submitted issue reports. In this se…
Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization
Saima Afrin, Zaiyu Cheng, Tushar Sharma +3
The rapid advancement of Large Language Models (LLMs) has revolutionized software engineering automation, particularly in automated code summarization, which enhances program compr…
Automatic Categorization of GitHub Actions with Transformers and Few-shot Learning
Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio +3
In the GitHub ecosystem, workflows are used as an effective means to automate development tasks and to set up a Continuous Integration and Delivery (CI/CD pipeline). GitHub Actions…
How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
Antonio Mastropaolo, Vittoria Nardone, Gabriele Bavota +1
Generative deep learning (DL) models have been successfully adopted for vulnerability patching. However, such models require the availability of a large dataset of patches to learn…
Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
Rosalia Tufano, Antonio Mastropaolo, Federica Pepe +3
Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through indus…
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization
Antonio Mastropaolo, Matteo Ciniselli, Massimiliano Di Penta +1
Several code summarization techniques have been proposed in the literature to automatically document a code snippet or a function. Ideally, software developers should be involved i…
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…
Toward Automatically Completing GitHub Workflows
Antonio Mastropaolo, Fiorella Zampetti, Gabriele Bavota +1
Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, whic…
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
Antonio Mastropaolo, Massimiliano Di Penta, Gabriele Bavota
Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a s…
Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier
Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio +3
Since its launch in November 2022, ChatGPT has gained popularity among users, especially programmers who use it as a tool to solve development problems. However, while offering a p…
Dealing with Popularity Bias in Recommender Systems for Third-party Libraries: How far Are We?
Phuong T. Nguyen, Riccardo Rubei, Juri Di Rocco +3
Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. Whi…
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…
On the Need of Removing Last Releases of Data When Using or Validating Defect Prediction Models
Aalok Ahluwalia, Massimiliano Di Penta, Davide Falessi
To develop and train defect prediction models, researchers rely on datasets in which a defect is attributed to an artifact, e.g., a class of a given release. However, the creation…