DT : a detection tool to automatically detect code smell in software project

Liu, Xinghua; Zhang, Cheng

doi:10.2991/icmmita-16.2016.126

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…This is bad practice leading a low software quality, as we mentioned above the "Duplicated Code" code smell. During the past few years, several studies have investigated two things: how to detect code smells [18], [19], [20], [21], [22], [23] and how to correct [5], [18], [24] them in a single software. To the best of our knowledge we found few studies [6], [8], [9], [10], [25], [26] that can be considered related to our research.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2021

IJSEA

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Software Product Lines (SPLs) refer to some software engineering methods, tools and techniques for creating a collection of similar software systems from a shared set of software assets using a common means of production. This concept is recognized as a successful approach to reuse in software development. Its purpose is to reduce production costs by reusing existing features and managing the variability between the different products with respect of particular constraints. Software Product Line engineering is the production process in product lines and the development of a family of systems by reusing core assets. It exploits the commonalities between software products and preserves the ability to vary the functionalities and features between these products. The adopted strategy for building SPL can be a top-down or bottom-up. Depending from the selected strategy, it is possible to face an inappropriate implementation in the SPL Model or the derived products during this process. The code can contain code smells or code anomalies. Code smells are considered as problems in source code which can have an impact on the quality of the derived products of an SPL. The same problem can be present in many derived products from an SPL due to reuse or in the obtained product line when the bottom-up strategy is selected. A possible solution to this problem can be the refactoring which can improve the internal structure of source code without altering external behavior. This paper proposes an approach for building SPL from source code using the bottom-up strategy. Its purpose is to reduce code smells in the obtained SPL using refactoring source code. This approach proposes a possible solution using reverse engineering to obtain the feature model of the SPL.

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Section: Related Workmentioning

confidence: 99%

Untitled

2021

IJSEA

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“…Therefore, it was better than other detection tools. Liu, X., et al, [10] research work related to the detection tools which was referred to detect the smell codes automatically in software system. Detection tools, metrics and threshold were described in this paper.…”

Section: Related Workmentioning

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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“…A smell detector is invented for both lab and industrial field by the research work [3]. It detects eleven types of code smells in both lab and industrial field.…”

Section: Related Workmentioning

confidence: 99%

“…Code smell is a type of code decay [1], [2] which hinders a developer for future maintenance of the system [3], [4]. It is good practice for developers to avoid the code smells because of maintaining in future of the system after development.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Four Selective Code Smells Declining in Real Life Projects

Erfan¹,

Halder²,

Pal³

et al. 2018

IJIEE

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Different types of codes which may increase the liability of bugs or defects in future to a system known as Code smell. These type of smell can be eliminated without changing the external outcome and modifying the internal structure of the system. There are existing several well known code smell detection tools which automatically identify the code smells. The research used PMD automatic code smell detector to find four code smells on various open source java projects. It uses 43 open source java projects and identify the selective smells to these projects. The experiment shows duplicate codes, unused imports, unused local variables and unused private methods are not present for 79%, 34.9%, 51.1%, and 86.04% projects respectively. In the paper, it also shows that the probability of occurring duplicate codes, unused imports, unused local variables and unused private methods are respectively 4.5%, 71%, 20.4% and 4.1% which indicates the four selective code smells are declining in real life projects day by day.Index Terms-Code smell, refactoring, detector and PMD. . He has been teaching a good number of courses related to computer networks, wireless and mobile systems, security, information technology project management, etc. to graduate and undergraduate students of reputed universities. He has research interests in wireless networking, wireless mesh networks, Information security, cloud computing, etc. He has published a good number of research papers in international conferences and journals.

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DT : a detection tool to automatically detect code smell in software project

Cited by 8 publications

References 4 publications

Untitled

Untitled

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

Experimental Study of Four Selective Code Smells Declining in Real Life Projects

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