RefDiff: Detecting Refactorings in Version Histories

Silva, Danilo; Valente, M.

doi:10.1109/msr.2017.14

Cited by 116 publications

(81 citation statements)

References 25 publications

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“…Given the amount of commits analyzed in this study, a manual detection of refactoring operations performed by developers would have been too expensive. In literature, two main approaches to automatically identify refactoring operations at commit level have been defined, i.e., Refac-toringMiner [61] and RefDiff [51]. In this study, we opt for the former because its accuracy has been shown to be higher than RefDiff [61].…”

Section: Extraction Of Refactoring Operationsmentioning

confidence: 99%

A large-scale empirical exploration on refactoring activities in open source software projects

Vassallo

Grano

Palomba

et al. 2019

Science of Computer Programming

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Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this paper, we continue on this line of research by performing a large-scale empirical analysis of refactoring practices in 200 open source systems. Specifically, we analyze the change history of these systems at commit level to investigate: (i) whether developers perform refactoring operations and, if so, which are more diffused and (ii) when refactoring operations are applied, and (iii) which are the main developer-oriented factors leading to refactoring. Based on our results, future research can focus on enabling automatic support for less frequent refactorings and on recommending refactorings based on the developer's workload, project's maturity and developer's commitment to the project.

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Section: Extraction Of Refactoring Operationsmentioning

confidence: 99%

A large-scale empirical exploration on refactoring activities in open source software projects

Vassallo

Grano

Palomba

et al. 2019

Science of Computer Programming

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“…Several tools and techniques are proposed in the literature to detect refactoring operations, for instance, Refactoring Crawler [14], RefFinder [15], Refactoring Miner [3], [5], and, more recently, RefDiff [13] and RMiner [16]. In common, those approaches only detect atomic refactoring, i.e., operations that happen in a single commit and performed by a single developer.…”

Section: A Detecting Refactoring Over Timementioning

confidence: 99%

“…In addition to be multi language, REFDIFF accuracy is quite high. REFDIFF's authors provide two evaluations of their tool [13]. In the first evaluation, it achieved an overall F-measure of 96.8% (precision: 100%; recall: 93.9%).…”

Section: Threats To Validitymentioning

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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“…In particular, previous studies on refactoring use source code changes history to detect and study refactoring changes. The approach by Silva et al [15] consists in 2 phases: (i) parse and analyze the history of source code changes to obtain a high level abstraction (i.e., a multiset of tokens); (ii) perform a relationship analysis, i.e., the procedure to find similarities between source code abstractions before and after the changes.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Context of Bug-Fixing Changes in the OpenStack Cloud Computing Platform

Cotroneo

Simone

Iannillo

et al. 2019

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)

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Many research areas in software engineering, such as mutation testing, automatic repair, fault localization, and fault injection, rely on empirical knowledge about recurring bug-fixing code changes. Previous studies in this field focus on what has been changed due to bug-fixes, such as in terms of code edit actions. However, such studies did not consider where the bug-fix change was made (i.e., the context of the change), but knowing about the context can potentially narrow the search space for many software engineering techniques (e.g., by focusing mutation only on specific parts of the software). Furthermore, most previous work on bug-fixing changes focused on C and Java projects, but there is little empirical evidence about Python software. Therefore, in this paper we perform a thorough empirical analysis of bug-fixing changes in three OpenStack projects, focusing on both the what and the where of the changes. We observed that all the recurring change patterns are not oblivious with respect to the surrounding code, but tend to occur in specific code contexts.

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RefDiff: Detecting Refactorings in Version Histories

Cited by 116 publications

References 25 publications

A large-scale empirical exploration on refactoring activities in open source software projects

A large-scale empirical exploration on refactoring activities in open source software projects

Refactoring Graphs: Assessing Refactoring over Time

Analyzing the Context of Bug-Fixing Changes in the OpenStack Cloud Computing Platform

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