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20112022
most citedAdversarial Robustness of Deep Code Comment Generation

49 citations · 196 across the 14 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

cs.SE2022★ 1 cited

DRIVE: Dockerfile Rule Mining and Violation Detection

Yu Zhou, Weilin Zhan, Zi Li +3

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable…

cs.SE2022

Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables

Jian Gu, Harald C. Gall

Deep code generation is a topic of deep learning for software engineering (DL4SE), which adopts neural models to generate code for the intended functions. Since end-to-end neural m…

cs.SE2022

Continuous Deep Learning: A Workflow to Bring Models into Production

Janosch Baltensperger, Pasquale Salza, Harald C. Gall

Researchers have been highly active to investigate the classical machine learning workflow and integrate best practices from the software engineering lifecycle. However, deep learn…

cs.SE2022★ 1 cited

Synthetic End-User Testing: Modeling Realistic Agents Based on Behavioral Examples

Pasquale Salza, Marco Edoardo Palma, Harald C. Gall

For software interacting directly with real-world end-users, it is common practice to script scenario tests validating the system's compliance with a number of its features. Howeve…

cs.SE2022★ 4 cited

On-the-Fly Syntax Highlighting using Neural Networks

Marco Edoardo Palma, Pasquale Salza, Harald C. Gall

With the presence of online collaborative tools for software developers, source code is shared and consulted frequently, from code viewers to merge requests and code snippets. Typi…

cs.SE2022★ 1 cited

Toward Granular Automatic Unit Test Case Generation

Fabiano Pecorelli, Giovanni Grano, Fabio Palomba +2

Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of…

cs.SE2022★ 2 cited

Assemble Foundation Models for Automatic Code Summarization

Jian Gu, Pasquale Salza, Harald C. Gall

Automatic code summarization is beneficial to daily software development since it could help reduce the requirement of manual writing. Currently, artificial intelligence is undergo…