A case study of refactoring large-scale industrial systems to efficiently improve source code quality

Refactoring source code has many benefits (e.g. improving maintainability, robustness and source code quality), but it takes time away from other implementation tasks, resulting in developers neglecting refactoring steps during the development process. But what happens when they know that the qualit...

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Bibliographic Details
Main Authors: Szőke Gábor
Nagy Csaba
Ferenc Rudolf
Gyimóthy Tibor
Format: Article
Published: Springer Verlag 2014
Series:LECTURE NOTES IN ARTIFICIAL INTELLIGENCE 8583 No. PART 5
doi:10.1007/978-3-319-09156-3_37

mtmt:2853688
Online Access:http://publicatio.bibl.u-szeged.hu/8996
Description
Summary:Refactoring source code has many benefits (e.g. improving maintainability, robustness and source code quality), but it takes time away from other implementation tasks, resulting in developers neglecting refactoring steps during the development process. But what happens when they know that the quality of their source code needs to be improved and they can get the extra time and money to refactor the code? What will they do? What will they consider the most important for improving source code quality? What sort of issues will they address first or last and how will they solve them? In our paper, we look for answers to these questions in a case study of refactoring large-scale industrial systems where developers participated in a project to improve the quality of their software systems. We collected empirical data of over a thousand refactoring patches for 5 systems with over 5 million lines of code in total, and we found that developers really optimized the refactoring process to significantly improve the quality of these systems. © 2014 Springer International Publishing.
Physical Description:524-540
ISSN:0302-9743