How does refactoring affect internal quality attributes?

Chávez, Alexander; Ferreira, Isabella; Fernandes, Eduardo; Cedrim, Diego; Garcia, Alessandro

doi:10.1145/3131151.3131171

Cited by 81 publications

(99 citation statements)

References 30 publications

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“…Moreover, most of these refactoring operations are applied to Multiple critical internal quality attributes, i.e., two or more attributes. It represents 79.43% (23,275) of the refactoring operations, against only 15.21% (4,458) for code elements with a Single critical internal quality attribute.…”

Section: Overall Resultsmentioning

confidence: 99%

“…This dissertation presents an empirical studies aimed at understanding the relationship of refactoring and re-refactoring operations with internal quality attributes. Our results were partially reported in a recent conference paper (23). We overview our studies as follows.…”

Section: Introductionmentioning

confidence: 90%

“…In a previous work (23), we partially address the aforementioned limitations. Our goal was empirically assessing the effects of refactoring operations on internal quality attributes.…”

Section: Introductionmentioning

confidence: 99%

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How Does Refactoring Affect Internal Quality Attributes?: A Multi-Project Study

LÓPEZ¹

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Section: Overall Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

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How Does Refactoring Affect Internal Quality Attributes?: A Multi-Project Study

LÓPEZ¹

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“…The previous study results are mixed and point out that, although single code transformations are prone to reduce bug introduction (2), bugs do not occur that far from the transformations in the commit history of software projects (21). Other studies show that not always the single code transformations are beneficial to code structures (8,10). In fact, single transformation tend to either introduce 3% or not fully remove 95% code smells (8).…”

Section: Empirical Evidence Of the Batch Effect On Code Maintenance Imentioning

confidence: 98%

“…The current knowledge about the types of batch effect on code maintenance is limited. In fact, the literature (2,8,9,10) has ultimately focused on assessing the effect of each single code transformation applied along refactoring rather than batches. Some studies like (2,21) have assessed to what extent code transformations prevent the introduction of software bugs.…”

Section: Empirical Evidence Of the Batch Effect On Code Maintenance Imentioning

confidence: 99%

Understanding Characteristics and Structural Effects of Batch Refactorings in Practice

BIBIANO¹

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First and foremost, I would like to thank God Almighty for the gift of living, growing up, and contributing the His plan the way I can. I also thank the Virgin Mary for being my role model and intercessor along the way. I am full of gratitude for my parents, Mrs. Lourinete Bibiano and Mr. Antonio Bibiano (in memoriam), my grandparents, brothers, and sister. Thanks for the kind support support. Especial thanks to Mr. José Pedro Luiz (in memoriam), my dear grandfather, for inspiring me to be a scientist. Thanks to all my friends for the support and patience along the years, especially Jedson Amaro, who helped me to reach my goals and was present in both happy and hard times, my dear friend Jakson Leao that brought me happiness and advice, and my brother Ladovanio Gomes that was my support all the time. Many people became special to my academic formation throughout this journey. I am grateful for their support along the last two years of study along my Master's course. I would like to thank Prof. Dr. Alessandro Garcia, my advisor, for believing in my potential as a scientist. Thanks for helping me reach my dreams. I also thank my dear friend MSc. Eduardo Fernandes who became my research colleague but also a friend for life. During the last two years, we grew together spiritually and academically. Thanks for believing and supporting me, even when I did not believe in myself. I am grateful for the reviews and improvements suggested to this Master's dissertation, including the literature review protocol. Thanks to Dr. Diego Cedrim e MSc. Isabella Ferreira for supporting me in the early phases of my research. You provided me with key support to build the code smells versus batch refactoring database. Thanks also to BS Daniel Oliveira for the support in collecting and validating data used in this work. I extend my thanks to all OPUS Research Group members and collaborators from other groups, especially Profs. Drs. Baldoino Fonseca (UFAL) and Marcos Kalinowski (PUC-Rio). I am thankful for your feedback aimed to shape the directions of my research. Especial thanks to the (anonymous) software development companies that provided me with data for analysis. Thanks to Profs. Drs. Leonardo Murta (UFF), Marcos Kalinowski (PUC-Rio), and Simone Barbosa (PUC-Rio) for kindly accepting to integrate this Master's dissertation defense committee. Doing science in Brazil is challenging and, in the context, I would like to thank all agencies that provided me with funding, especially the Brazilian National Council for Scientific and Technological Development (CNPq), plus the partner companies of the Software Engineering Laboratory (LES/PUC-Rio) for the student scholarship.

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Code quality improvement using Aquila Optimizer

Mohamed

Salah

Eldefrawi

2023

J Software Evolu Process

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The software industry always requires high‐quality software. Code quality has been improved via refactoring and re‐refactoring and removing code smells. Refactoring is a technique to improve existing code by modifying inner structure, while code smells are indication that might have a wrong in code that causes a negative impact on the code quality. According to previous studies, the accuracy of code smell detection was not stable and could be improved by advanced algorithms. Additionally, the impact of refactoring on each of the quality attributes is unclear. This research proposes the use of Aquila Optimizer to detect code smells in addition to investigate the refactoring and re‐refactoring impact through ISO 9126 model. The experiment is conducted on five open‐sources Java projects to detect nine code smell types and measure the values of the ISO 9126 before and after the refactoring and re‐refactoring techniques to show the impact on code quality. The results showed that the average of detection accuracy is 95.18% and 95.68% for precision and recall, respectively. Moreover, the results in large projects are better than in the medium projects. The findings also show improvement in the values of the ISO 9126 model after applying refactoring and re‐refactoring.

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How does refactoring affect internal quality attributes?

Cited by 81 publications

References 30 publications

How Does Refactoring Affect Internal Quality Attributes?: A Multi-Project Study

How Does Refactoring Affect Internal Quality Attributes?: A Multi-Project Study

Understanding Characteristics and Structural Effects of Batch Refactorings in Practice

Code quality improvement using Aquila Optimizer

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