Towards Effective Teams for the Identification of Code Smells

Mello, Rafael de; Oliveira, Roberto; Sousa, Leonardo; Garcia, Alessandro

doi:10.1109/chase.2017.11

Cited by 6 publications

(2 citation statements)

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“…Code review is a software engineering activity directly related to the pull-based development model. Recent studies have investigated the code review activity from different perspectives [42,59,41,77]. Uchôa et al [77], for example, observed that discussions about design may not be enough to avoid design decay and that certain social-related factors, such as long discussions, are responsible for increasing design decay symptoms.…”

Section: Design Decay and Its Symptomsmentioning

confidence: 99%

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Exploring the Social Aspects of Design Decay

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and an Android developer as hobby. I have a bachelor's degree in Computer Science from the Federal University of Alagoas (UFAL). During my graduation, I was always part of research projects. I participated on collaborations with relevant European Universities, such as University of Coimbra, University of Florence, which I visited for three months as part of the collaboration, and University of College London. My current research is focuses on understanding if social aspects that surround the collaborative code development have a relation with design decay.

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Section: Design Decay and Its Symptomsmentioning

confidence: 99%

“…Social aspects in code review. There are multiple studies about the influence of social aspects during code review [42,59,41,77]. For instance, Ruangwan et.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Social Aspects of Design Decay

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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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