A Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

Kádár, István; Hegedűs, Péter; Ferenć, Rudolf; Gyimóthy, Tibor

doi:10.1109/saner.2016.42

Cited by 23 publications

(5 citation statements)

References 16 publications

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“…It is worth noting that we focus our attention on a relative small number of software systems because, as detailed in Section II-A, we relied on a publicly available dataset composed of 12,922 operations (manually validated) related to 28 different refactoring types identified in each of the considered releases [18]. Note that while other datasets are available [21], to the best of our knowledge the one built by Bavota et al is the largest one in terms of refactoring operations (12,922 vs 7,872 reported by Kadar et al).…”

Section: Empirical Study Definition and Designmentioning

confidence: 99%

An Exploratory Study on the Relationship between Changes and Refactoring

Palomba

Zaidman

Oliveto

et al. 2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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Refactoring aims at improving the internal structure of a software system without changing its external behavior. Previous studies empirically assessed, on the one hand, the benefits of refactoring in terms of code quality and developers' productivity, and on the other hand, the underlying reasons that push programmers to apply refactoring. Results achieved in the latter investigations indicate that besides personal motivation such as the responsibility concerned with code authorship, refactoring is mainly performed as a consequence of changes in the requirements rather than driven by software quality. However, these findings have been derived by surveying developers, and therefore no software repository study has been carried out to corroborate the achieved findings. To bridge this gap, we provide a quantitative investigation on the relationship between different types of code changes (i.e., Fault Repairing Modification, Feature Introduction Modification, and General Maintenance Modification) and 28 different refactoring types coming from 3 open source projects. Results showed that developers tend to apply a higher number of refactoring operations aimed at improving maintainability and comprehensibility of the source code when fixing bugs. Instead, when new features are implemented, more complex refactoring operations are performed to improve code cohesion. Most of the times, the underlying reasons behind the application of such refactoring operations are represented by the presence of duplicate code or previously introduced self-admitted technical debts

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Section: Empirical Study Definition and Designmentioning

confidence: 99%

An Exploratory Study on the Relationship between Changes and Refactoring

Palomba

Zaidman

Oliveto

et al. 2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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“…The dataset utilized in this was adopted from a previous study of 34,35 that provided extensive information regarding refactoring and source code metrics for 7 open-source Java systems (Table 1). It is composed of 37 releases of these systems and is characterized by a comprehensive manual validation process that guarantees the accuracy and reliability of the data.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The dataset used in this research encompasses a range of refactoring types, with 23 classes and 19 methods. The data repository is available from 34,35 at http:// www. inf.u-szeged.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Visualizing software refactoring using radar charts

Al-Ghuwairi,

Al-Fraihat,

Sharrab

et al. 2023

Sci Rep

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Refactoring tools have advanced greatly and are being used in many large projects. As a result, a great deal of information is now available about past refactoring and its effects on the source code. However, when multiple refactoring is performed at once, it becomes more difficult to analyze their impact. Refactoring visualization can help developers create more maintainable code that is easier to understand and modify over time. Although there is an increasing interest in visualizing code changes in software engineering research, there has been relatively little research on visualizing the process of refactoring. In this paper, we propose a Radar Chart Refactoring Visualization (RcRV) approach to visualize software refactoring of source code across multiple software releases. Radar charts are a form of 2D visualization that can show multiple variables on a single chart. The RcRv receives input from developers or through refactoring identification tools, such as Ref-Finder, to generate charts. The generated charts can show the changes made during the refactoring process, highlighting areas of the trend of refactoring over evolution for multiple refactoring, multiple methods, and multiple classes. The evaluation study conducted to assess the usefulness of the RcRV tool has shown that the proposed tool is useful to developers, appealing, and easy to use. The proposed method of visualization can be beneficial for developers and maintainers to detect design violations and potential bugs in the code, thus saving time and effort during the development and maintenance process. Therefore, this research presents a significant contribution to the software engineering field by providing developers with an efficient tool to enhance code quality and maintainability.

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“…Kadar et al [54] put forward the idea to examine code refactoring as a means of creating an open dataset that would be necessary for utilizing software code metrics and refactoring techniques across various versions of seven systems. Through the use of refactoring methods, the authors aimed to analyze the quality features of duplicate source code classes, as well as the effectiveness of updating source code metrics.…”

Section: Related Research To the Quality Of Codementioning

confidence: 99%

A literature review for measuring maintainability of code clone

Khaleel,

Al-Khatouni

2023

IJEECS

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Software organizations face constant pressure due to stakeholder requirements and the increasing complexity of software systems. This complexity, combined with defects in code quality and failures, can pose risks to software systems. To ensure code is understood before maintenance, developers must spend over 60% of their time modifying and improving code quality, which is costly. This study examines the impact of code refactoring activities on software maintainability and quality by reviewing relevant research and explaining key terms. The research finds that refactoring activities can enhance specific quality characteristics, including maintainability, understandability, and testability. The study also identifies important factors that should be considered when developing refactoring tools. Refactoring enables code improvement without altering program behavior and can be applied multiple times to source code.

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A Code Refactoring Dataset and Its Assessment Regarding Software Maintainability

Cited by 23 publications

References 16 publications

An Exploratory Study on the Relationship between Changes and Refactoring

An Exploratory Study on the Relationship between Changes and Refactoring

Visualizing software refactoring using radar charts

A literature review for measuring maintainability of code clone

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