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  1. Proceedings of the The 12th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2016)
  2. A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability
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Forecasting Communication Behavior in Student Software Projects
Estimating Story Points from Issue Reports
Search Based Training Data Selection For Cross Project Defect Prediction
On the Terms Within- and Cross-Company in Software Effort Estimation
Measuring the Stylistic Inconsistency in Software Projects using Hierarchical Agglomerative Clustering
An Empirical Evaluation of Distribution-based Thresholds for Internal Software Measures
Data Sets and Data Quality in Software Engineering: Eight Years On
Hidden Markov Models for the Prediction of Developer Involvement Dynamics and Workload
Predicting the Popularity of GitHub Repositories
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

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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