Replication data for "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair"

Name: Replication data for "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair"
Published: 2024-07-15
License: http://creativecommons.org/licenses/by/4.0

Open this dataset in the live repository

Important usage condition: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

Persistent identifier: doi:10.60507/FK2/O54LWP
Published version: 2.0
Publication date: 2024-07-15
License: CC BY 4.0

Description

This repository contains replication data for the paper "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair".<br> The dataset contains commits that indicate a bug fix from 200 repositories on Github. Scripts for fetching data associated with the commits are available (see Github repository linked under "Related Material"). <hr> File descriptions: <ul> <li><code>repositories.csv</code>: List of repository names with metadata</li> <li><code>commits.csv</code>: List of commit identifiers of the 200 repositories with most bug fixing commits</li> <li><code>commits_high_watch_count.csv</code>: List of commit identifiers of the 200 repositories with most bug fixing commits with a watch count of at least 50</li> </ul> <hr> Icon licensed under CC-BY by xinh.studio

Creators

Leiwig, Maximilian
Swierzy, Ben
Bungartz, Christian
Meier, Michael

Keywords

Program Repair, Machine Learning, Large Language Models

Files

Before downloading: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

File	Type	Bytes	Checksum
repositories.csv	text/csv	3144907	MD5 f578867bd4854a1b228f335aa1b75d26
commits_high_watch_count.csv	text/csv	1567538	MD5 64524b826cd44b2abf7064b794dbe6fe
commits.csv	text/csv	6963520	MD5 32a7d4bbb62765d708713b2ca8d6f5a8
README.md	text/markdown	1945	MD5 bd4306963175ff314ccc46d7601a2b35

Citation

Leiwig, Maximilian; Swierzy, Ben; Bungartz, Christian; Meier, Michael, 2024-07-15, Replication data for "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair", doi:10.60507/FK2/O54LWP, V2.0

Additional Dataverse fields

Id	368
Dataset Type	dataset
Internal Version Number	7
Latest Version Publishing State	RELEASED
Deaccession Link	Not supplied
Release Time	2025-01-14T11:07:50Z
Create Time	2025-01-13T15:17:32Z
Citation Date	2024-07-15
File Access Request	True

Export metadata

Static metadata exports available for this published dataset version:

Complete Dataverse metadata

Citation Metadata

Title title

Replication data for "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair"

Author author

Author Name
Leiwig, Maximilian
Author Affiliation
University of Bonn, Lamarr Institute
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0009-0006-5703-5708
Expandedvalue
Person Name
Leiwig, Maximilian
@id
https://orcid.org/0009-0006-5703-5708
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Swierzy, Ben
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0009-0003-0485-4791
Expandedvalue
Person Name
Swierzy, Ben
@id
https://orcid.org/0009-0003-0485-4791
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Bungartz, Christian
Author Affiliation
University of Bonn, Lamarr Institute
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0009-0008-0576-8744
Expandedvalue
Person Name
Bungartz, Christian
@id
https://orcid.org/0009-0008-0576-8744
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Meier, Michael
Author Affiliation
University of Bonn, Fraunhofer FKIE, Lamarr Institute
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
primitive
Value
https://orcid.org/0009-0006-8199-5004
Expandedvalue
Person Name
Meier, Michael
@id
https://orcid.org/0009-0006-8199-5004
Scheme
ORCID
@type
https://schema.org/Person

Dataset Contact datasetContact

Dataset Contact Name
Leiwig, Maximilian
Dataset Contact Affiliation
University of Bonn
Dataset Contact Email
s6maleiw@uni-bonn.de

Ds Description dsDescription

Ds Description Value
This repository contains replication data for the paper "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair".<br> The dataset contains commits that indicate a bug fix from 200 repositories on Github. Scripts for fetching data associated with the commits are available (see Github repository linked under "Related Material"). <hr> File descriptions: <ul> <li><code>repositories.csv</code>: List of repository names with metadata</li> <li><code>commits.csv</code>: List of commit identifiers of the 200 repositories with most bug fixing commits</li> <li><code>commits_high_watch_count.csv</code>: List of commit identifiers of the 200 repositories with most bug fixing commits with a watch count of at least 50</li> </ul> <hr> Icon licensed under CC-BY by xinh.studio

Subject subject

Computer and Information Science

Subject Refinement subjectRefinement

Subject Refinement Value
Computer Science: Software Engineering and Programming Languages [409-02]

Free Keyword freeKeyword

Free Keyword Value
Program Repair
Free Keyword Value
Machine Learning
Free Keyword Value
Large Language Models

Publication publication

Publication Relation Type
IsSupplementTo
Publication Citation
M. Leiwig, B. Swierzy, C. Bungartz and M. Meier, "Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair," 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Paris, France, 2024, pp. 72-79, doi: 10.1109/SEAA64295.2024.00020.
Publication IDType
doi
Publication IDNumber
10.1109/SEAA64295.2024.00020.
Publication URL
https://doi.org/10.1109/SEAA64295.2024.00020

Notes Text notesText

The corresponding paper was be published at SEAA Aug 28 - Aug 30 2024.

Language language

Not applicable

Depositor depositor

Leiwig, Maximilian

Date Of Deposit dateOfDeposit

2024-07-08

Kind Of Data kindOfData

quantitative

Related Material relatedMaterial

Code: https://github.com/Synrom/FixMe

Access To Sources accessToSources

Original Sources are Google BigQuery and GitHub

Expected crawler behaviour

Use a stable, truthful User-Agent with product/version and a working contact URL. Across all IP addresses and HTTP connections used by one crawler identity, allow no more than 5 requests in flight and wait at least 20 seconds between request starts. Crawl URLs listed in the catalog sitemap, including file pages and download URLs when they are published, use conditional requests, honor Retry-After, and apply exponential backoff after errors.

The welcome page may link to the interactive repository for human navigation. Automated clients must not treat that human link as a catalog crawl target.

Read the live machine-readable crawler policy before and during a crawl. Stop crawling when it reports CPU or memory utilization at or above 80% and 80% respectively.