On the use of textual feature extraction techniques to support the automated detection of refactoring documentation

Marmolejos, Licelot; AlOmar, Eman Abdullah; Mkaouer, Mohamed Wiem; Newman, Christian D.; Ouni, Ali

doi:10.1007/s11334-021-00388-5

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…All errors detected during the testing phase are fixed, the agreed additions/changes to the system's functionality are also made, and the corrections are made to its documentation [37,38]:…”

Section: Testing Of the Developed Information Systemmentioning

confidence: 99%

About the life cycle of information systems based on open Web-technologies

Kratov¹,

Sokolova²

2021

3rd International Workshop on Information, Computation, and Control Systems for Distributed Environments 2021

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The article describes the full life cycle of an information system based on open Webtechnologies from the point of view of developers and system administrators. The article starts with the development of technical specifications for an information system creation and ends with the system’s launching into operation and its subsequent uninterrupted operation. As the example, the creation process of the information system for the Foundation of Algorithms and Programs of the Siberian Branch of the Russian Academy of Sciences is described.

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Section: Testing Of the Developed Information Systemmentioning

confidence: 99%

About the life cycle of information systems based on open Web-technologies

Kratov¹,

Sokolova²

2021

3rd International Workshop on Information, Computation, and Control Systems for Distributed Environments 2021

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“…This classification challenges the original definition of refactoring, being exclusive to improving software design and fixing code smells. (Marmolejos et al, 2021) proposed a framework to identify refactoring documentation by using different techniques, such as feature hashing and feature selection (Chi-squared and Fisher score), and five machine learning algorithms. As per their results, the combination of Chi-Squared with Bayes point machine and Fisher score with Bayes point machine could be the most efficient when it comes to automatically identifying refactoring documentation, with an F-measure of 96%.…”

Section: Refactoring Documentationmentioning

confidence: 99%

On the Documentation of Refactoring Types

AlOmar¹,

Liu²,

Mkaouer³

et al. 2021

Preprint

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Commit messages are the atomic level of software documentation. They provide a natural language description of the code change and its purpose. Messages are critical for software maintenance and program comprehension. Unlike documenting feature updates and bug fixes, little is known about how developers document their refactoring activities. Specifically, developers can perform multiple refactoring operations, including moving methods, extracting classes, renaming attributes, for various reasons, such as improving software quality, managing technical debt, and removing defects. Yet, there is no systematic study that analyzes the extent to which the documentation of refactoring accurately describes the refactoring operations performed at the source code level. Therefore, this paper challenges the ability of refactoring documentation, written in commit messages, to adequately predict the refactoring types, performed at the commit level. Our analysis relies on the text mining of commit messages to extract the corresponding features (i.e., keywords) that better represent each class (i.e., refactoring type). The extraction of text patterns, specific to each refactoring type (e.g., rename, extract, move,

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