Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects

Cedrim, Diego; Garcia, Alessandro; Mongiovi, Melina; Gheyi, Rohit; Sousa, Leonardo; Mello, Rafael de; Fonseca, Baldoíno; Ribeiro, Márcio; Chávez, Alexander

doi:10.1145/3106237.3106259

Cited by 63 publications

(85 citation statements)

References 31 publications

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“…For this study, we have decided to detect code smells with metric-based strategies (92,30). Thus, we can compare our results with related studies that have used the same detection strategies (85,88). These strategies are based on a set of metrics and thresholds, in which the metric values are compared against predefined thresholds and combined using logical operators.…”

Section: Phase 3: Code Smell Detectionmentioning

confidence: 96%

“…The selected projects are written in Java. We focused on Java projects because (i) Java is a very popular programming language 2 , and Java projects were also targeted by related studies (85,88). Furthermore, we also selected projects in Java due to the availability of tools to identify refactoring operations (89).…”

Section: Phase 1: Selection Of Software Projectsmentioning

confidence: 99%

“…For this purpose, we collected metric (values) for all source files in each selected project from Phase 1. Next, we collected the code smells by applying a set of previously defined detection rules (85,30) used in related studies (85,88). Unfortunately, the detection rules were not implemented by any publicly available tool.…”

Section: Phase 3: Code Smell Detectionmentioning

confidence: 99%

“…Cedrim et al (88) also investigated the frequency with which refactoring operations are applied to smelly elements. They also found that 79.4% of refactoring operations are applied to smelly elements.…”

Section: Applying Refactoring Operationsmentioning

confidence: 99%

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Understanding How Developers Identify Design Problems in Practice

SOUSA¹

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Section: Phase 3: Code Smell Detectionmentioning

confidence: 96%

Section: Phase 1: Selection Of Software Projectsmentioning

confidence: 99%

Section: Phase 3: Code Smell Detectionmentioning

confidence: 99%

Section: Applying Refactoring Operationsmentioning

confidence: 99%

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Understanding How Developers Identify Design Problems in Practice

SOUSA¹

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“…Another example is Move Method, which consists of moving a method across classes (66). This transformation type is usually applied to allocate a method to the most appropriate class (8).…”

Section: Introductionmentioning

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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Exploring the relationship between refactoring and code debt indicators

Halepmollasi

Tosun

2022

J Software Evolu Process

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Refactoring, which aims to improve the internal structure of the software systems preserving their behavior, is the most common payment strategy for technical debt (TD) by removing the code smells. There exist many studies presenting code smell detection approaches/tools or investigating their impact on quality attributes. There are also studies that focus on refactoring techniques, their relation with quality attributes, tool supports, and opportunities for them. Although there are several studies addressing the gap between refactoring and TD indicators, the empirical evidence provided is still limited. In this study, we examine the distribution of 29 refactoring types among the different projects and their relation with code smells or faults. We explore the refactoring types that are most commonly performed together and other activities performed with refactorings. We conduct a large exploratory study with automatically detected 57,528 refactorings, 37,553 smells, 27,340 faults, and 134,812 commits of 33 Java projects. Results show that some refactoring types are more commonly applied by developers. Our analysis indicates that refactorings usually remove or do not affect the code smells, and this contradicts with the previous studies. Also, the commits in which refactoring(s) is performed are three times more fault inducing than those without refactoring.

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Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects

Cited by 63 publications

References 31 publications

Understanding How Developers Identify Design Problems in Practice

Understanding How Developers Identify Design Problems in Practice

Understanding Characteristics and Structural Effects of Batch Refactorings in Practice

Exploring the relationship between refactoring and code debt indicators

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