On the evaluation of code smells and detection tools

Paiva, Thanis; Damasceno, Amanda; Figueiredo, Eduardo; Sant’Anna, Cláudio

doi:10.1186/s40411-017-0041-1

Cited by 68 publications

(57 citation statements)

References 25 publications

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“…We relied on the Sharma and Spinellis survey as our key witness for the relevance of the question of high rates of false positives. A more thorough discussion of the work on false positives would be very desirable, especially with respect to those publications that made the effort to construct manually validated datasets [3,28,29].…”

Section: Related Workmentioning

confidence: 99%

“…Here we do need the access to public static fields and methods themselves but not the access to members of public static fields. 28 The reductions per class are: java.lang.Math = 67 (calls to max(int,int) = 21, min(int,int) = 9, access to PI = 8, and others), CH. We did not rule out the access to all of the members of these classes but only the accesses to globally accessible members.…”

Section: :39mentioning

confidence: 99%

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Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Speicher

2020

Programming

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Developers need to make a constant effort to improve the quality of their code if they want to stay productive. Tools that highlight code locations that could benefit from refactoring are thus highly desirable. The most common name for such locations is "bad code smell". A number of tools offer such quality feedback and there is a substantial body of related research.However, all these tools, including those based on Machine Learning, still produce false positives. Every single false positive shown to the developer places a cognitive burden on her and should thus be avoided. The literature discusses the choice of metric thresholds, the general subjectivity of such a judgment and the relation to conscious design choices, "design ideas".To examine false positives and the relation between bad smells and design ideas, we designed and conducted an exploratory case study. While previous research presented a broad overview, we have chosen a narrow setting to reach for even deeper insights: The framework JHotDraw had been designed so thoughtfully that most smell warnings are expected to be false positives. Nevertheless, the "Law of Good Style", better known as the "Law of Demeter", is a rather restrictive design rule so that we still expected to find some potential bad smells, i.e. violations of this "Law".This combination led to 1215 potential smells of which at most 42 are true positives. We found generic as well as specific design ideas that were traded for the smell. Our confidence in that decision ranged from high enough to very high. We were surprised to realize that the smell definition itself required the formulation of constructive design ideas. Finally we found some smells to be the result of the limitation of the language and one could introduce auxiliary constructive design ideas to compensate for them.The decision whether a potential smell occurrence is actually a true positive was made very meticulously. For that purpose we took three qualities that the smell could affect negatively into account and we discussed the result of the recommended refactorings. If we were convinced that we had found a false positive, we described the relationships with design ideas.The realization that not only general design ideas but also specific design ideas have an influence on whether a potential smell is a true positive turns the problem of false positives from a scientific problem ("What is the true definition of the smell?") to a engineering problem ("How can we incorporate design ideas into smell definitions?"). We recommend to add adaptation points to the smell definitions. Higher layers may then adapt the smell for specific contexts. After adaptation the tool may continuously provide distinct and precise quality feedback, reducing the cognitive load for the developer and preventing habituation. Furthermore, the schema for the discussion of potential smells may be used to elaborate more sets of true and false smell occurrences. Finally, it follows that smell detection based on machine learning should also take signs of...

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Section: Related Workmentioning

confidence: 99%

Section: :39mentioning

confidence: 99%

Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Speicher

2020

Programming

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“…Shariful Islam, and Rahat Hossain Faisal automated code smells detection tools PMD, CheckStyle, JDeodorant, inFusion and so on. inFusion is a smell detection tool which is standalone tool [13]. It is possible to detect different type ofThis tool can detect22 code smells and also.…”

Section: Experimental Study Of Four Selective Code Smells Declining Imentioning

confidence: 99%

“…In [13], the authors use three detection tools for finding code smells are inFusion, JDeodorant and PMD for finding three code smells God class, God method and Feature envy and comparing those tools with different open source projects. The author [10] reviews the current panorama of the tools for automatic code smell detection.…”

Section: Experimental Study Of Four Selective Code Smells Declining Imentioning

confidence: 99%

“…State of the art research works are evaluated for open source projects with different version. In [13], three detection tools for finding code smells are inFusion, JDeodorant and PMD used to find three code smells God class, God method and Feature envy and comparing those tools with different open source projects. They These tools are used Mobile Media system to find the code smells and evaluate the result using 9 versions of a project.…”

Section: Related Workmentioning

confidence: 99%

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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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