On the Alternatives for Composing Batch Refactoring

Fernandes, Eduardo; Uchôa, Anderson; Bibiano, Ana Carla; Garcia, Alessandro

doi:10.1109/iwor.2019.00009

Cited by 14 publications

(16 citation statements)

References 14 publications

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“…Among the empirical studies, some research focus on set of related refactoring. Specifically, these studies analyze batch refactorings [1], [8], [9], [50]- [52]. Murphy et al [1] analyzed four datasets from different sources, all of these including metadata about the usage of Eclipse IDE.…”

Section: Related Workmentioning

confidence: 99%

Refactoring Graphs: Assessing Refactoring over Time

Brito

Hora

Valente

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Refactoring is an essential activity during software evolution. Frequently, practitioners rely on such transformations to improve source code maintainability and quality. As a consequence, this process may produce new source code entities or change the structure of existing ones. Sometimes, the transformations are atomic, i.e., performed in a single commit. In other cases, they generate sequences of modifications performed over time. To study and reason about refactorings over time, in this paper, we propose a novel concept called refactoring graphs and provide an algorithm to build such graphs. Then, we investigate the history of 10 popular open-source Java-based projects. After eliminating trivial graphs, we characterize a large sample of 1,150 refactoring graphs, providing quantitative data on their size, commits, age, refactoring composition, and developers. We conclude by discussing applications and implications of refactoring graphs, for example, to improve code comprehension, detect refactoring patterns, and support software evolution studies.

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Section: Related Workmentioning

confidence: 99%

Refactoring Graphs: Assessing Refactoring over Time

Brito

Hora

Valente

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…Batch refactoring consists of applying sets of interrelated code transformations on the code structures of software project (7). These transformations are interrelated in such a way that they may help developers to fully reach a common goal, such as fully removing poor code structures that harm code maintenance (20,64). Each set of interrelated code transformations is called a batch (7).…”

Section: Resultsmentioning

confidence: 99%

“…The source code is publicly available at GitHub for consultation 1,2,3 . Other well-documented examples of real batches applied on open source projects can be found in recent studies like (19) and (20). The example below is intended to illustrate the application of a non-trivial batch in practice.…”

Section: Batch Refactoring At a Glancementioning

confidence: 99%

“…However, applying these sets of transformations can be challenging for developers due to various reasons. Indeed, applying each transformation requires a careful analysis of the code structure and how the refactored code elements interrelate to realize a software feature (8,20,25). Developers may also reason about how the applied batch will probably affect the code structure (8).…”

Section: Batch Refactoring At a Glancementioning

confidence: 99%

“…By the paper titles, we observed that most of the previous studies have either proposed or evaluated optimization strategies for composing batch refactoring in practice. In fact, understanding how to compose batches that achieve varied goals while enhancing code structures is important to support developers in their daily work (20). 37An empirical study of refactoring: Challenges and benefits at Microsoft (35) Composite refactorings for Java projects (12) Designing and developing automated refactoring transformations (65) DRACO: Discovering refactorings that improve architecture using fine-grained co-change dependencies (14) Evolving transformation sequences using genetic algorithms (15) Experimental assessment of software metrics using automated refactoring (47) FaultBuster: An automatic code smell refactoring toolset (64) How we refactor, and how we know it (46) Identifying refactoring sequences for improving software maintainability (43) Improving refactoring speed by 10x (34) Interactive and guided architectural refactoring with search-based recommendation (39) Pareto optimal search based refactoring at the design level (30) Recommendation system for software refactoring using innovization and interactive dynamic optimization (44) Refactoring with synthesis (58) Scripting parametric refactorings in Java to retrofit design patterns (33) Search-based detection of high-level model changes (5) Search-based refactoring based on unfolding of graph transformation systems (57) Search-based refactoring detection (41) Search-based refactoring using recorded code changes (50) Search-based refactoring: Towards semantics preservation (49) Searching for opportunities of refactoring sequences: Reducing the search space (55) Template-based reconstruction of complex refactorings (56) The use of development history in software refactoring using a multi-objective evolutionary algorithm (51) TrueRefactor: An automated refactoring tool to improve legacy system and application comprehensibility (28) *Understanding and Improving Batch Refactoring in Software Systems (7) WitchDoctor: IDE support for real-time auto-completion of refactorings (24) Figure 3.2 presents the paper publication rate across the years, based on the 29 selected papers.…”

Section: Study Stepsmentioning

confidence: 99%

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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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Model‐based source code refactoring with interaction and visual cues

Hemati Moghadam,

Ó Cinnéide,

Sardarian

et al. 2023

J Software Evolu Process

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Refactoring source code involves the developer in a myriad of program detail that can obscure the design changes that they actually wish to bring about. On the other hand, refactoring a UML model of the code makes it easier to focus on the program design, but the burdensome task of applying the refactorings to the source code is left to the developer. In an attempt to obtain the advantages of both approaches, we propose a refactoring approach where the interaction with the developer takes place at the model level, but the actual refactoring occurs on the source code itself. We call this approach model‐based source code refactoring and implement it in this paper using two tools: (1) Design‐Imp enables the developer to use interactive search‐based design exploration to create a UML‐based desired design from an initial design extracted from the source code. It also provides visual cues to improve developer comprehension during the design‐level refactoring process and to help the developer to discern between promising and poor refactoring solutions. (2) Code‐Imp then refactors the original source so that it has the same functional behavior as the original program, and a design close to the one produced in the design exploration phase, that is, a design that has been confirmed as “desirable” by the developer. We evaluated our approach involving interaction and visual cues with industrial developers refactoring three Java projects, comparing it with an approach using interaction without visual cues and a fully automated approach. The results show that our approach yields refactoring sequences that are more acceptable both to the individual developer and to a set of independent expert refactoring evaluators. Furthermore, our approach removed more code smells and was evaluated very positively by the experiment participants.

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On the Alternatives for Composing Batch Refactoring

Cited by 14 publications

References 14 publications

Refactoring Graphs: Assessing Refactoring over Time

Refactoring Graphs: Assessing Refactoring over Time

Understanding Characteristics and Structural Effects of Batch Refactorings in Practice

Model‐based source code refactoring with interaction and visual cues

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