JDeodorant: Identification and Removal of Feature Envy Bad Smells

Fokaefs, Marios; Tsantalis, Nikolaos; Chatzigeorgiou, Alexander

doi:10.1109/icsm.2007.4362679

Cited by 95 publications

(63 citation statements)

References 3 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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“…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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“…With the goal of detecting OO code and design related issues, a number of tools have been introduced in the literature [2,5,6]. Nevertheless, researchers and developers have rarely considered tools to perform detection for SOA antipatterns, i.e., in SBSs.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the automatic detection of such SOA antipatterns is an important activity to assess the design and QoS of SBSs, and thus ease the maintenance and evolution tasks of the engineers. However, a number of works have been devoted for the development of detection tools within Object Oriented (OO) systems [2,5,6]. Yet, for the detection of SOA antipatterns in SBSs, there is no tool support.…”

Section: Introductionmentioning

confidence: 99%

Soda: A Tool Support for the Detection of SOA Antipatterns

Nayrolles

Palma

Moha

et al. 2013

Service-Oriented Computing - ICSOC 2012 Workshops

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Abstract. During their evolution, Service Based Systems (SBSs) need to fit new user requirements and execution contexts. The resulting changes from the evolution of SBSs may degrade their design and Quality of Service (QoS), and thus may cause the appearance of common poor solutions, called Antipatterns. Like other complex systems, antipatterns in SBSs may hinder the future maintenance and evolution. Therefore, the automatic detection of such antipatterns is an important task for assessing the design and QoS of SBSs, to facilitate their maintenance and evolution. However, despite of their importance, no tool support exists for the detection of antipatterns in SBSs. In this paper, we introduce a prototype tool, called Soda, for detecting SOA (Service Oriented Architecture) antipatterns in SBSs.

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JDeodorant: Identification and Removal of Feature Envy Bad Smells

Cited by 95 publications

References 3 publications

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

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

Automated style feedback for advanced beginner Java programmers

Soda: A Tool Support for the Detection of SOA Antipatterns

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