Impact of refactoring on quality code evaluation

Fontana, Francesca Arcelli; Spinelli, Stefano

doi:10.1145/1984732.1984741

Cited by 33 publications

(18 citation statements)

References 9 publications

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“…DuBois et al studied the impact of refactorings on cohesion and coupling metrics in [7] and found that benets can occur, and described how and when the application of refactoring could improve selected quality characteristics [6]. Fontana et al studied the impact of refactoring applied to reduce code smells on the quality evaluation of the system [9].…”

Section: Related Workmentioning

confidence: 99%

A Case Study of Refactoring Large-Scale Industrial Systems to Efficiently Improve Source Code Quality

Szőke

Nagy

Ferenć

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. Refactoring source code has many benets (e.g. improving maintainability, robustness and source code quality), but it takes time away from other implementation tasks, resulting in developers neglecting refactoring steps during the development process. But what happens when they know that the quality of their source code needs to be improved and they can get the extra time and money to refactor the code? What will they do? What will they consider the most important for improving source code quality? What sort of issues will they address rst or last and how will they solve them? In our paper, we look for answers to these questions in a case study of refactoring large-scale industrial systems where developers participated in a project to improve the quality of their software systems. We collected empirical data of over a thousand refactoring patches for 5 systems with over 5 million lines of code in total, and we found that developers really optimized the refactoring process to signicantly improve the quality of these systems.

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Section: Related Workmentioning

confidence: 99%

A Case Study of Refactoring Large-Scale Industrial Systems to Efficiently Improve Source Code Quality

Szőke

Nagy

Ferenć

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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“…Similar to this approach, Arcelli et al [AFS11], according to well known metrics for code and design quality evaluation, analyze the impact of refactoring applied to remove some code smells on the quality evaluation of these metrics, with the aim to prioritize the smells to be removed.…”

Section: Code Smells Evolutionsmentioning

confidence: 99%

Automatic detection of bad smells in code: An experimental assessment.

Fontana¹,

Braione²,

Zanoni³

2012

JOT

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Code smells are structural characteristics of software that may indicate a code or design problem that makes software hard to evolve and maintain, and may trigger refactoring of code. Recent research is active in defining automatic detection tools to help humans in finding smells when code size becomes unmanageable for manual review. Since the definitions of code smells are informal and subjective, assessing how effective code smell detection tools are is both important and hard to achieve. This paper reviews the current panorama of the tools for automatic code smell detection. It defines research questions about the consistency of their responses, their ability to expose the regions of code most affected by structural decay, and the relevance of their responses with respect to future software evolution. It gives answers to them by analyzing the output of four representative code smell detectors applied to six different versions of GanttProject, an open source system written in Java. The results of these experiments cast light on what current code smell detection tools are able to do and what the relevant areas for further improvement are.

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“…Despite the high acceptance of refactoring techniques by the software industry, it has been shown that practitioners apply code refactoring differently than Fowler originally suggested. He proposed that code smells should be the primary technique for identifying refactoring opportunities in the code and a lot of research effort [2,3,4,5] has been put into examining them. However, there are statements in the literature [6,7,8] that engineers are aware of code smells, but are not really concerned on their impact as refactoring activity is not focused on them.…”

Section: Introductionmentioning

confidence: 99%

Empirical evaluation of software maintainability based on a manually validated refactoring dataset

Hegedűs

Kádár

Ferenć

et al. 2018

Information and Software Technology

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Context: Refactoring is a technique for improving the internal structure of software systems. It has a solid theoretical background while being used in development practice also. However, we lack empirical research results on the real effect of code refactoring and its application. Objective: This paper presents a manually validated subset of a previously published dataset containing the refactorings extracted by the RefFinder tool, code metrics, and maintainability of 7 open-source systems. We found that RefFinder had around 27% overall average precision on the subject systems, thus our manually validated subset has substantial added value. Using the dataset, we studied several aspects of the refactored and non-refactored source code elements (classes and methods), like the differences in their maintainability and source code metrics. Method: We divided the source code elements into a group containing the refactored elements and a group with non-refactored elements. We analyzed the elements' characteristics in these groups using correlation analysis, Mann-Whitney U test and effect size measures. Results: Source code elements subjected to refactorings had significantly lower maintainability than elements not affected by refactorings. Moreover, refactored elements had significantly higher size related metrics, complexity, and coupling.

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Impact of refactoring on quality code evaluation

Cited by 33 publications

References 9 publications

A Case Study of Refactoring Large-Scale Industrial Systems to Efficiently Improve Source Code Quality

A Case Study of Refactoring Large-Scale Industrial Systems to Efficiently Improve Source Code Quality

Automatic detection of bad smells in code: An experimental assessment.

Empirical evaluation of software maintainability based on a manually validated refactoring dataset

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