Which refactoring reduces bug rate?

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1 Scopus citations

Abstract

We present a methodology to identify refactoring operations that reduce the bug rate in the code. The methodology is based on comparing the bug fixing rate in certain time windows before and after the refactoring. We analyzed 61, 331 refactor commits from 1, 531 large active GitHub projects. When comparing three-month windows, the bug rate is substantially reduced in 17% of the files of analyzed refactors, compared to 12% of the files in random commits. Within this group, implementing 'todo's provides the most benefits. Certain operations like reuse, upgrade, and using enum and namespaces are also especially beneficial.

Original languageAmerican English
Title of host publicationPROMISE 2019 - 15th International Conference on Predictive Models and Data Analytics in Software Engineering
PublisherAssociation for Computing Machinery
Pages12-15
Number of pages4
ISBN (Electronic)9781450372336
DOIs
StatePublished - 18 Sep 2019
Event15th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2019, co-located with the 13th International Symposium on Empirical Software Engineering and Measurement, ESEM 2019 - Recife, Brazil
Duration: 18 Sep 2019 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2019, co-located with the 13th International Symposium on Empirical Software Engineering and Measurement, ESEM 2019
Country/TerritoryBrazil
CityRecife
Period18/09/19 → …

Bibliographical note

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).

Keywords

  • Code quality
  • Machine learning
  • Refactoring

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