Using software metrics to select refactoring for long method bad smell

Meananeatra, Panita; Rongviriyapanish, Songsakdi; Apiwattanapong, Taweesup

doi:10.1109/ecticon.2011.5947882

Cited by 21 publications

(18 citation statements)

References 4 publications

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“…Meananeatra et al [30] propose the decomposition of source code using the abstract syntax tree (i.e., data flow and control flow graphs) and the proposition of Extract Method opportunities based on the calculation of complexity and/or cohesion metrics. Specifically, Meananeatra et al, proposed an approach aiming at resolving the Long Method smell by applying several refactorings (not only the Extract Method one).…”

Section: Extract Methods Identificationmentioning

confidence: 99%

“…In particular, such approaches cannot be accurate for cases in which the assumption does not hold, e.g., a developer makes excessive or limited use of blank lines.  Approaches based only on the abstract syntax tree (i.e., the iteration and decision nodes of the code) [41] and [30]-Approaches that are only based on the abstract syntax tree, might miss Extract Method opportunities, since some potentially large code fragments are considered as blocks and are not further examined. For example, consider the case that a method consists of multiple  it is the first study that investigates the functional relevance of source code fragments to identify Extract Method opportunities.…”

Section: Comparison To Related Workmentioning

confidence: 99%

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Identifying Extract Method Refactoring Opportunities Based on Functional Relevance

Charalampidou

Ampatzoglou

Chatzigeorgiou

et al. 2017

IIEEE Trans. Software Eng.

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Extract Method' is considered one of the most frequently applied and beneficial refactorings, since the corresponding Long Method smell is among the most common and persistent ones. Although Long Method is conceptually related to the implementation of diverse functionalities within a method, until now, this relationship has not been utilized while identifying refactoring opportunities. In this paper we introduce an approach (accompanied by a tool) that aims at identifying source code chunks that collaborate to provide a specific functionality, and propose their extraction as separate methods. The accuracy of the proposed approach has been empirically validated both in an industrial and an open-source setting. In the former case, the approach was capable of identifying functionally related statements within two industrial long methods (approx. 500 LoC each), with a recall rate of 93%. In the latter case, based on a comparative study on open-source data, our approach ranks better compared to two well-known techniques of the literature. To assist software engineers in the prioritization of the suggested refactoring opportunities the approach ranks them based on an estimate of their fitness for extraction. The provided ranking has been validated in both settings and proved to be strongly correlated with experts' opinion.

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Section: Extract Methods Identificationmentioning

confidence: 99%

Section: Comparison To Related Workmentioning

confidence: 99%

Identifying Extract Method Refactoring Opportunities Based on Functional Relevance

Charalampidou

Ampatzoglou

Chatzigeorgiou

et al. 2017

IIEEE Trans. Software Eng.

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“…• Most tools are available in form of plug-in which make them dependent on other software. • Tools are constraint to a particular language [18], [19].…”

Section: Gaps In Researchmentioning

confidence: 99%

Design of testing framework for code smell detection (OOPS) using BFO algorithm

Sharma

Kaur

2018

IJET

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Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.

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“…The actual application of these methodologies relies on a certain list of preconditions to identify high conditional complexity without versatility. Recent researches show that software metrics are regarded as important indicators of refactoring in many cases, and are used to analyze code smells such as Feature Envy, Pure Data Class, Large Class, Long Method, Shotgun Surgery (Marinesc, 2004;Lanza, Marinescu and Mihancea et al, 2005;Lanza, Marinescu and Ducasse, 2006;Higo, Kusumoto and Inoue, 2008;Meananeatra and Rongviriyapanish, 2011;Ferreira, Bigonha and Bigonha et al, 2012). Some metrics are closely associated with high conditional complexity hindering identification of refactoring opportunities.…”

Section: Introductionmentioning

confidence: 99%

Identification of High Conditional Complexity in Source Code Based on Statistical Analysis

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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In order to identify the high conditional complexity in source code, a novel approach based on statistical analysis is proposed. According to the statistical analysis of two software metrics which are Method McCabe's Cyclomatic Complexity (MMCC) and Method Average McCabe's Cyclomatic Complexity Per Code Line (MAMCC) in a large number of projects, the probability density functions and cumulative distribution functions for describing distributions of these two metrics are obtained. Moreover, a model for identifying high conditional complexity is built by choosing reasonable threshold of these metrics, and this model can be used for preliminary screening the methods which have high MMCC and high MAMCC. The experimental results show that the proposed approach can identify some candidate methods which need to be refactored accurately.

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Using software metrics to select refactoring for long method bad smell

Cited by 21 publications

References 4 publications

Identifying Extract Method Refactoring Opportunities Based on Functional Relevance

Identifying Extract Method Refactoring Opportunities Based on Functional Relevance

Design of testing framework for code smell detection (OOPS) using BFO algorithm

Identification of High Conditional Complexity in Source Code Based on Statistical Analysis

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