Code smells detection 2.0: Crowdsmelling and visualization

Reis, José Pereira dos; Abreu, Fernando Brito e; Carneiro, Glauco de Figueiredo

doi:10.23919/cisti.2017.7975961

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…In (Reis et al, 2017) we have proposed the concept of Crowdsmelling -use of collective intelligence in the detection of code smells -to mitigate the aforesaid problems of subjectivity and lack of calibration data required to obtain accurate detection model parameters. In this paper we reported first results of a study investigating the approach Crowdsmelling, a collaborative crowdsourcing approach, based in machine learning, where the wisdom of the crowd (of software developers) will be used to collectively calibrate code smells detection algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…tagged datasets for training detection algorithms. To mitigate this hindrance, we have proposed Crowdsmelling (Reis et al, 2017), a collaborative crowdsourcing approach, based in machine learning, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms. The applications based in collective intelligence, where the contribution of several users allows attaining benefits of scale and/or other types of competitive advantage, are gaining increasing importance in Software Engineering (Stol and Fitzgerald, 2014) and other areas (Bigham et al, 2014;Bentzien et al, 2013).…”

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Crowdsmelling: A preliminary study on using collective knowledge in code smells detection

2022

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Code smells are seen as a major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigating the problem of smells-infected code.This paper presents the results of a validation experiment for the Crowdsmelling approach proposed earlier. The latter is based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue.In the context of three consecutive years of a Software Engineering course, a total "crowd" of around a hundred teams, with an average of three members each, classified the presence of 3 code smells (Long Method, God Class, and Feature Envy) in Java source code. These classifications were the basis of the oracles used for training six machine learning algorithms. Over one hundred models were generated and evaluated to determine which machine learning algorithms had the best performance in detecting each of the aforementioned code smells.Good performances were obtained for God Class detection (ROC=0.896 for Naive Bayes) and Long Method detection (ROC=0.870 for AdaBoostM1), but much lower for Feature Envy (ROC=0.570 for Random Forrest).The results suggest that Crowdsmelling is a feasible approach for the detection of code smells. Further validation experiments based on dynamic learning are required to comprehensive coverage of code smells to increase external validity.

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Section: Discussionmentioning

confidence: 99%

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Crowdsmelling: A preliminary study on using collective knowledge in code smells detection

2022

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“…(5) Thresholds for deciding on CS occurrence are often arbitrary/unsubstantiated and not generalizable; in mitigation, we foresee the potential for the application of multi-criteria approaches that take into account the scope and context of CS, as well as approaches that explore the power of the crowd, such as the one proposed in [46]; (6) CS studies in mobile and web environments are still scarce; due to their importance of those environments in nowadays life, we see a wide berth for CS research in those areas;…”

Section: Open Issuesmentioning

confidence: 99%

Code Smells Detection and Visualization: A Systematic Literature Review

Reis

Abreu

Carneiro

et al. 2021

Arch Computat Methods Eng

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Context: Code smells tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Researchers have argued that due to the subjectiveness of the code smells detection process, proposing an effective use of automatic support for this end is a non trivial task.Objective: This Systematic Literature Review (SLR) has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former.Method: Over eighty primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them.Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), metric-based (24.1%), and symptom-based approaches (19.3%). Most of the studies (83.1%) use open-source software, with

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