Reproducing and Improving the BugsInPy Dataset

Faustino Aguilar, Samuel Grayson, Darko Marinov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We assess the reproducibility of the BugsInPy dataset less than three years after its original publication. The bug dataset provides some information about the software environment in which the code should be run, but this information can be incomplete or can decay into something uninstallable over time. We rectify as many of these problems as we can and redesign the original dataset to be more easily reusable and reproducible by future research projects. Based on our experience, we offer suggestions to authors of Python artifacts to improve their reproducibility.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation, SCAM 2023
EditorsLeon Moonen, Christian Newman, Alessandra Gorla
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages260-264
Number of pages5
ISBN (Electronic)9798350305067
DOIs
StatePublished - 2023
Event23rd IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2023 - Bogota, Colombia
Duration: Oct 1 2023Oct 2 2023

Publication series

NameProceedings - 2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation, SCAM 2023

Conference

Conference23rd IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2023
Country/TerritoryColombia
CityBogota
Period10/1/2310/2/23

Keywords

  • BugsInPy
  • Conda
  • Docker
  • Pip
  • Python
  • bug database
  • containers
  • package managers
  • reproducibility

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Safety, Risk, Reliability and Quality

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