Identification and application of Extract Class refactorings in object-oriented systems

Fokaefs, Marios; Tsantalis, Nikolaos; Stroulia, Eleni; Chatzigeorgiou, Alexander

doi:10.1016/j.jss.2012.04.013

Cited by 130 publications

(98 citation statements)

References 10 publications

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“…Through using the detection tool JDeodorant, code smell research field has produed lot of papers [4,3]. It can automatically detect 4 code smells : Feature Envy, God Class, Long Method and Switch Statement.…”

Section: Discussionmentioning

confidence: 99%

The detection of code smell on software development: a mapping study

Liu¹,

Cheng²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract.Context: Although Code Smell can't cause problems with execution of project code, Code Smell can cause some potential problems of maintainability and understandability on the software projects. Meanwhile, as for the research of code smell, current research results pay attention to only several specific Code Smells, and then don't have a comprehensive detection on Code Smell. Objective: To investigate what the objective of Code Smell study is, and to find what kinds of code smells could the detection tools of code smell detect. Methods: According to the Guidelines of Kithenham, we carry out a mapping study about 22 code smells, searching the relevant papers till 2015. Results: Through the process of mapping study, 286 papers are finally included and then classified into our data records. Conclusion: Referring to detection tools, firstly they only take notice of several specific Code Smells, because these code smells can be easily measured in term of quantification. Secondly, experiment systems of these papers are almost lab projects and industrial open source not the industrial closed source projects. Thirdly, the size of most detected lab projects are under 30 KLOC. In the future, we will focus efforts on detection of Code Smells that can't be easily detected, what's more, we will put our studies under a comprehensive environment, using three types of project: lab project, open source industrial project and closed source industrial project.

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Section: Discussionmentioning

confidence: 99%

The detection of code smell on software development: a mapping study

Liu¹,

Cheng²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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“…JDeodorant has been deemed as "the only software currently available able to provide code smell detection and then to suggest which refactoring to apply to remove on the detected smells" [17]. The developers of JDeodorant provided extensive documentation on how the detection of certain code smells has been implemented [46,15,16]. In order to refactor 'Duplicate Code' smells a third party tool is needed to detect clones.…”

Section: Instrumentationmentioning

confidence: 99%

“…On the other hand, we cannot guarantee that using a different tool for refactoring the smells would give similar results. Nevertheless, the implementation of the refactoring processes is thoroughly reported in the tool documentation [46,15,16]. In addition, to the best of our knowledge, JDeodorant is to date the only tool that supports automated detection and refactoring of the smells we selected as treatments [17].…”

Section: Conclusion Validitymentioning

confidence: 99%

Empirical Evaluation of the Energy Impact of Refactoring Code Smells

Verdecchia

Saez²,

Procaccianti

et al.

EPiC Series in Computing

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Software energy efficiency has gained the increasing attention of the research community. How to improve it, however, still lacks evidence. Specifically, the impact of code smell refactoring on energy efficiency has been scarcely investigated. In the exploratory study here reported, we investigate the impact on performance and energy consumption of refactoring well-known code smells on Java software applications. In order to understand if software metrics can be used as indicators of the energy impact of refactoring, we also measured the variation caused by refactoring on a set of well-established software metrics. We conducted a controlled experiment using state-of-the-art power measurement equipment. Statistical hypothesis testing and effect size estimation were performed on the experimental results, which show that in one out of three applications, refactoring each smell significantly impacted power-and energy consumption. E.g., refactoring Feature Envy and Long Method smells led to a 49% energy efficiency improvement. No software metric, however, significantly correlated with execution time, power or energy consumption. In conclusion, refactoring code smells resulted to be a viable process to significantly improve software energy efficiency. The magnitude of the impact may depend on application properties, e.g. size or age. Further research is needed to understand the relationship between software metrics and energy efficiency.

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“…These methods are declared public but never called outside their own class: runTrials (14) printResult (25) If you meant these to be helper methods used only within this class, they should be declared private instead.…”

Section: Frenchpress In Actionmentioning

confidence: 99%

“…FrenchPress is similar in spirit to systems such as Stench Blossom [26] and JDeodorant [13,38,14,21] that alert programmers to code smells, questionable program fea-…”

Section: Code Smellsmentioning

confidence: 99%

Automated style feedback for advanced beginner Java programmers

Blau

Kolovson

Adrion

et al. 2016

2016 IEEE Frontiers in Education Conference (FIE)

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ACKNOWLEDGMENTSFrenchPress is an Eclipse plug-in that partially automates the task of giving students feedback on their Java programs. It is designed not for novices but for students taking their second or third Java course: students who know enough Java to write a working program but lack the judgment to recognize bad code when they see it. FrenchPress does not diagnose compile-time or run-time errors, or logical errors that produce incorrect output. It targets silent flaws, flaws the student is unable to identify for himself because nothing in the programming environment alerts him. among students who said FrenchPress gave them suggestions for improvement, the percentage who found the feedback helpful bounced from around 55% on the first and third assignments to 32-40% on the second and fourth assignments. The lower acceptance on the second and fourth projects corresponds to a higher incidence of false positives and other confusing feedback messages. Nevertheless, the percentage of survey respondents who said they were satisfied with FrenchPress performance ranged from 56% to 66% on all four assignments.vi

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Identification and application of Extract Class refactorings in object-oriented systems

Cited by 130 publications

References 10 publications

The detection of code smell on software development: a mapping study

The detection of code smell on software development: a mapping study

Empirical Evaluation of the Energy Impact of Refactoring Code Smells

Automated style feedback for advanced beginner Java programmers

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