Conflict-aware schedule of software refactorings

Liu, H.; Li, G.; Ma, Zhiyi; Shao, Wei

doi:10.1049/iet-sen:20070033

Cited by 29 publications

(27 citation statements)

References 37 publications

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“…Liu et al 10 take a list of refactoring candidates to be scheduled as input and produce an appropriate application order on which to apply more refactorings, especially the most effective refactorings in order to maximize the quality improvement. To do this, they first select all refactoring candidates which do not disable application of other refactoring candidates.…”

Section: Related Workmentioning

confidence: 99%

“…However, most research on refactoring focuses on how to apply a refactoring operator individually 9; there is little research on scheduling refactoring 10. In this paper, we propose an approach to determining an appropriate refactoringschedule to maximize quality improvement.…”

Section: Introductionmentioning

confidence: 99%

“…The quality improvement can be measured by comparing software quality before and after refactoring 10, 20–23. It is also possible to estimate the effect of refactoring on quality before it is applied 24–26.…”

Section: Introductionmentioning

confidence: 99%

“…It is also possible to estimate the effect of refactoring on quality before it is applied 24–26. The estimation can be done by experts manually 24, 26 or by computer automatically using quality assessment models 10, 20–23.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the QMOOD quality model was validated through empirical experiments and expert opinion by the authors 29. It has also been widely used for assessing quality improvement achieved by refactoring 10, 20–22. Especially, O'Keeffe and Cinnéide 21 investigated three quality attributes—reusability, flexibility, and understandability—in use for assessment of quality improvement in search‐based software refactoring.…”

Section: Introductionmentioning

confidence: 99%

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Automated scheduling for clone‐based refactoring using a competent GA

et al. 2010

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Refactoring is a widely accepted technique to improve the software quality by restructuring its design without changing its behavior. In general, a sequence of refactorings needs to be applied until the quality of the code is improved satisfactorily. In this case, the final design after refactoring can vary with the application order of refactorings, thereby producing different quality improvements. Therefore, it is necessary to determine a proper refactoring schedule to obtain as many benefits as possible. However, there is little research on the problem of generating appropriate schedules to maximize quality improvement. In this paper, we propose an approach to automatically determine an appropriate schedule to maximize quality improvement through refactoring. We first detect code clones that are suitable for refactoring and generate the most beneficial refactoring schedule to remove them. It is straightforward to select the best from the exhaustively enumerated schedules. However, such a technique becomes NP‐hard, as the number of available refactorings increases. We apply a genetic algorithm (GA) to generate the best refactoring schedule within a reasonable time to cope with this problem. We compare the GA‐based approach with manual scheduling, greedy heuristic‐based, and exhaustive approaches for four open systems. The results show that the proposed GA‐based approach generates more beneficial schedules than the others. Copyright © 2010 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated scheduling for clone‐based refactoring using a competent GA

et al. 2010

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Software refactoring side effects

AbuHassan

Alshayeb

Ghouti

2021

J Software Evolu Process

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Software refactoring solutions aim at mitigating the negative effects of code and design smells on the overall software quality. Many efforts have been exerted to improve the software refactoring process. However, most of these efforts, despite their contributions, overlooked the side effects of the identified refactoring opportunities that may lead to new smells that will go unnoticed. This paper addresses the side effects of software refactoring and proposes sound solutions for handling them. Unlike current practices in software maintenance, we recommend three different approaches to handle the refactoring side effects. In the first approach, called the baseline, we opt to ignore the smells, caused by refactoring, while executing the identified refactoring decisions. In the second one, refactoring decisions are continually updated to fix all smells caused by side effects. In the last approach, only a subset of these smells is appended to the original smell sequence during the execution of the refactoring decisions. Thanks to the proposed approaches, optimal refactoring decisions are identified using a multi‐objective (MO) optimization algorithm commonly known as the MO covariance matrix adaptation evolution strategy (MO‐CMA‐ES). Experiment results corroborate our assumptions and show the superiority of the second approach over the other ones.

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