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20202026
most citedAn Empirical Study on the Usage of Transformer Models for Code Completion

98 citations · 160 across the 15 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.SE2023

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…

cs.SE2023★ 59 cited

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…

cs.SE2023

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…

cs.SE2023

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…

cs.SE2023★ 2 cited

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…

cs.SE2023★ 1 cited

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…