Which refactoring reduces bug rate?

Idan Amit; Dror G. Feitelson

doi:10.1145/3345629.3345631

Which refactoring reduces bug rate?

Idan Amit
, Dror G. Feitelson

The Rachel and Selim Benin School of Engineering and Computer Science

The Hebrew University of Jerusalem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	PROMISE 2019 - 15th International Conference on Predictive Models and Data Analytics in Software Engineering
Pages	12-15
Number of pages	4
ISBN (Electronic)	9781450372336
DOIs	https://doi.org/10.1145/3345629.3345631
State	Published - 18 Sep 2019
Event	15th 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

Name	ACM International Conference Proceeding Series

Conference

Conference	15th 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/Territory	Brazil
City	Recife
Period	18/09/19 → …

Keywords

Code quality
Machine learning
Refactoring

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3345629.3345631

Cite this

@inproceedings{421a284314d644478264e0d8bba29d1d,

title = "Which refactoring reduces bug rate?",

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.",

keywords = "Code quality, Machine learning, Refactoring",

author = "Idan Amit and Feitelson, \{Dror G.\}",

note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 15th 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 ; Conference date: 18-09-2019",

year = "2019",

month = sep,

day = "18",

doi = "10.1145/3345629.3345631",

language = "English",

series = "ACM International Conference Proceeding Series",

pages = "12--15",

booktitle = "PROMISE 2019 - 15th International Conference on Predictive Models and Data Analytics in Software Engineering",

}

Amit, I & Feitelson, DG 2019, Which refactoring reduces bug rate? in PROMISE 2019 - 15th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM International Conference Proceeding Series, pp. 12-15, 15th 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, 18/09/19. https://doi.org/10.1145/3345629.3345631