A case study on the effects and limitations of refactoring

Bekefi, Bianka Flora; Szabados, Kristóf; Kovács, Attila

doi:10.1109/informatics47936.2019.9119321

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Therefore, it is essential for developers and researchers to carefully consider this particular drawback in any refactoring tool before using it in practice or developing a new refactoring approach. Békefi et al [46] empirical study that investigated the effect and the limitations of refactoring using a new proposed refactoring tool also supports this observation, which highlights the critical need to take this limitation into consideration when evaluating and selecting refactoring tools.…”

Section: Discussionmentioning

confidence: 71%

A Comparative Study of Automated Refactoring Tools

Alharbi,

Alshayeb

2024

IEEE Access

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Researchers proposed several refactoring approaches supported by automated and semiautomated refactoring tools. However, the existence of numerous automated refactoring tools imposes difficulties on developers to decide upon the appropriate one according to their needs. Moreover, the performance of the existing refactoring tools has not been empirically evaluated against the other available tools targeting the same refactoring opportunities. Therefore, the objective of this research is to conduct a comparative study to systematically compare and evaluate refactoring tools that belong to different categories of refactoring approaches. To this end, we propose an evaluation framework based on the DESMET methodology. The framework is used to empirically compare and evaluate four different refactoring tools, namely MultiRefactor, JDeodorant, jSparrow, and Spartenizer, using five open-source projects. The evaluation results show that jSparrow outperforms the other investigated tools by supporting the highest number of quantitative and qualitative features, suggesting that it is the best choice based on various perspectives. On the other hand, Spartenizer demonstrated the least favorable outcomes in terms of both quantitative and qualitative features, including introducing new code smells after applying a refactoring opportunity. The findings of this comparative study would assist the developers in understanding the characteristics and the capability of the studied refactoring tools. Also, it benefits the researchers to focus their efforts on addressing the identified limitations to enhance the smell detection and refactoring process.

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Section: Discussionmentioning

confidence: 71%

A Comparative Study of Automated Refactoring Tools

Alharbi,

Alshayeb

2024

IEEE Access

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“…In addition to their survey analysis, Békefi et al provided a case study survey where a refactoring tool is developed. 32 This tool identifies and removes Feature Envy code smell through the application of Move Method refactoring. Potential destinations of the moved methods are evaluated by three distinct approaches.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, if the current sequence consists of eight smells (say smells with IDs:[12,24,32,56, 63, 69, 81, 98]), four smells are fixed by refactoring decisions (say smells with IDs:[24,56, 63, 98]) and five new ones emerge as side effects (say smells with IDs:[6,11,45, 73, 91]). In this case, the remaining sequence has length of four.…”

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confidence: 99%

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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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Android Source Code Smells: A Systematic Literature Review

Fawad,

Rasool,

Palma

2024

Softw Pract Exp

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IntroductionModern software developers strive to develop applications that are robust, easy to maintain, and possess high levels of quality. However, code smells can hinder this goal as they are visible signs of underlying issues. Numerous techniques and tools have been proposed for detecting code smells in various contexts and programming languages. Despite this, research on Android‐specific code smells and their impact on external quality attributes is still in its early stages.ObjectiveThis study aims to provide a comprehensive summary of state‐of‐the‐art techniques, tools, and approaches used for detecting and refactoring code smells in Android applications.MethodologyA systematic literature review was conducted between November 2007 and December 2023, adhering to standard guidelines. In total, 79 primary studies were identified, analyzed, and synthesized.ResultsA total of 237 code smells were identified using 51 techniques and tools, based on seven distinct approaches. Efficiency was found to be the most affected external quality attribute. The code smell Durable Wakelock was the most studied.ChallengesDespite the extensive research, the software engineering community faces numerous challenges. These include a lack of in‐depth investigation into Android‐specific code smells, a limited number of studied quality attributes, insufficient involvement of industry experts in the research process, scarcity of Android‐oriented metrics, and dependence on industry‐exclusive datasets.Future DirectionsThis review suggests potential research directions that are valuable for researchers and practitioners seeking to gain insights into current open research problems in this domain.

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A case study on the effects and limitations of refactoring

Cited by 5 publications

References 10 publications

A Comparative Study of Automated Refactoring Tools

A Comparative Study of Automated Refactoring Tools

Software refactoring side effects

Android Source Code Smells: A Systematic Literature Review

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