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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"

Authors: Francesco Altiero; Anna Corazza; Sergio Di Martino; Adriano Peron; Luigi Libero Lucio Starace;

Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"

Abstract

This is the replication package for the paper "AI-based Fault-proneness Metrics for Source Code Changes", submitted at the IWSM-Mensura '23 conference. The archive is a Docker image file with a fully setup and working environment to re-execute the experiments involved in the manuscript. We pre-loaded all libraries and codeBERT models to ease the replication process and avoid compatibility issues, as the environment cannot be easily managed using Dockerfiles. To run the image, a Docker installation is needed. Once downloaded, from the command line type: docker load -i </path/to/downloaded/ai-proneness-replication.tar> After the loading process, you can run the container by typing: docker run -it mensura/ai-proneness-replication:1.0 All the source code and the dataset to re-execute the experiment is located into the /Replication folder. The folder contains the results of our experimentation in CSV and MS Excel format, along with the following subdirectories: dataset: a replication of the used dataset. The file dataset.csv gives information on all the entries, while the code folder contains a subdirectory for each sample, named by its id. In the folder, the file old.txt and new.txt refers to the older and newer version of the method, respectively; gitdiff.txt stores the raw git-diff command output, while diff.html stores a more human-readable version of the differences. ai-fault-proneness-tk-replication: the Java code used to apply Tree Kernel techniques on the dataset (we used JDK-11, embedded within the container). To build and execute the package, refer to the file README.md in the folder. For convenience, we also provided an executable JAR file ai-fault-proneness-tk-replication-1.0-jar-with-dependencies.jar that can be run directly and saves the output in a CSV file in the results folder of the replication package. code-embeddings-and-analysis: python scripts to execute the codeBERT-based approaches and to extract the diff statistics. To execute all the steps, a convenience shell script execute.sh has been pre-loaded and can be executed to automatize all the process.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average