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.
BibTeX:
@InProceedings{KHF16b,
author = {K\'{a}d\'{a}r, Istv\'{a}n and Heged\H{u}s, P{\'e}ter and Ferenc, Rudolf and Gyim\'{o}thy, Tibor},
title = {A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability},
booktitle = {Proceedings of the 12th ACM International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2016)},
year = {2016},
pages = {10:1--10:4},
address = {Ciudad Real, Spain},
month = sep,
publisher = {ACM},
doi = {10.1145/2972958.2972962},
keywords = {Code refactoring, empirical study, manually validated empirical dataset, software maintainability},
url = {https://dl.acm.org/citation.cfm?doid=2972958.2972962},
}