NDLI: A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

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A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

Content Provider	ACM Digital Library
Author	Ferenc, Rudolf Kádár, István Gyimóthy, Tibor Hegedűs, Péter
Abstract	Refactoring is a popular technique for improving the internal structure of software systems. It has a solid theoretical background while being used in development practice at the same time. However, we lack empirical research results on the real effect of code refactoring and its ways of application. This paper presents a manually validated dataset of applied refactorings and source code metrics and maintainability of 7 open-source systems. It is a subset of our previously published dataset containing the refactoring instances automatically extracted by the RefFinder tool. We found that RefFinder had around 27% overall average precision on the subject systems, thus our new -- manually validated -- subset has substantial added value allowing researchers to perform more accurate empirical investigations. Using this data, we were able to study whether refactorings were really triggered by poor maintainability of the code, or by other aspects. The results show that source code elements subject to refactorings had significantly lower maintainability values (approximated by source code metric aggregation) than elements not affected by refactorings between two releases.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450347723
DOI	10.1145/2972958.2972962
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Code refactoring Empirical study Software maintainability Manually validated empirical dataset
Content Type	Text
Resource Type	Article

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