Code smell detection by deep direct-learning and transfer-learning

Sharma, Tushar; Efstathiou, Vasiliki; Λουρίδας, Πάνος; Spinellis, Diomidis

doi:10.1016/j.jss.2021.110936

Cited by 75 publications

(33 citation statements)

References 51 publications

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“…For detecting the CS [10] proposed a DL structure with two specific models. In this method, the DL structure was employed with two layers of CNN and RNN as well as Auto Encoders to modulate the performance to fine-tune the smell detection performances of different CS.…”

Section: Literature Surveymentioning

confidence: 99%

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

2022

IJIES

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Code smells (CS) are a severe symptom in software source code that leads to a serious problem in software maintenance and evolution. Feature Envy (FE) is a type of CS that refers to methods that are misplaced in the source code. Many tools like infusion, JDeodorant and JspIRIT have been used to identify the CS. The conversion of source code measurements into predictions is based on manually constructed heuristics. But, manually selecting good features and constructing optimal heuristics is a challenging issue. So, automatic detection of CS is required. The Deep Learning (DL) methods play an important role in achieving the automatic detection of FE(DLFED). The FE detection approach based on DL is intended to discover misplaced methods. The DL technique automatically identifies the source code characteristics for FE identification as well as automatically improves the difficult mapping structure between the features and their prediction. But, a DL-based automatic detection of CS requires a significant number of features with labeled training datasets to provide better prediction results. Therefore, an extension of the DL-based FE detection technique (EDLFED-FM) is proposed in this article to detect both misplaced methods and fields. The semantic relationship between identifiers in the source code program is used to extract the features of misplaced fields and methods. The new decomposition slice method is proposed to convert the extracted features of misplaced methods and fields into the slicing vector composition. This sliced feature helps deep learners understand the relationship between misplaced methods and fields. This automated method is capable of producing labeled training data for DL techniques-based classifiers without the requirement for human intervention. Finally, the experimental outcomes proved that precision rate of suggested method outperforms the JDeodorant and DLFED methods by 14.49% and 10.29 % for free plane, 14.72 % and 12.2 % for Junit, and 21.84% and 14.68 % for JExcelapi applications. Hence, it has been demonstrated that the predicted misplaced methods and fields are moved to the refactoring class for positive testing of source code program with less training data.

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Section: Literature Surveymentioning

confidence: 99%

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

2022

IJIES

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“…Oliveira et al [235] relied on historical data and mined smell instances from history where the smells are refactored. Some efforts such as one by Sharma et al [283] used CodeSplit [281,282] first to split source code files into individual classes and methods. Then, they used existing smell detection tools [280,284] to identify smells in the subject systems.…”

Section: Code Smell Detectionmentioning

confidence: 99%

“…Similarly, Ochodek et al [233] analyzed individual lines in source code to extract textual properties such as regex and keywords to formulate a set of vocabulary based features (such as bag of words). Furthermore, Sharma et al [283] hypothesized that dl methods can infer the features by themselves and hence explicit feature extraction is not required. They did not process the source code to extract features and feed the tokenized code to ml models.…”

Section: Code Smell Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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“…After 2010 research activity regarding Design Smell detection has experienced a rapid and huge growth. Different approaches and techniques have been proposed with respect to the identification and correction of Design Smells ranging from manual, semi-automated to fully automated [9,13,14,15,16,17,18,19,20]. In addition, most of the approaches were mapped into detection tools with different capabilities and deal with different programming languages.…”

Section: Introductionmentioning

confidence: 99%

Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

et al. 2021

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The automatic detection of Design Smells has evolved in parallel to the evolution of automatic refactoring tools. There was a huge rise in research activity regarding Design Smell detection from 2010 to the present. However, it should be noted that the adoption of Design Smell detection in real software development practice is not comparable to the adoption of automatic refactoring tools. On the basis of the assumption that it is the objectiveness of a refactoring operation as opposed to the subjectivity in definition and identification of Design Smells that makes the difference, in this paper, the lack of agreement between different evaluators when detecting Design Smells is empirically studied. To do so, a series of experiments and studies were designed and conducted to analyse the concordance in Design Smell detection of different persons and tools, including a comparison between them. This work focuses on two well known Design Smells: God Class and Feature Envy. Concordance analysis is based on the Kappa statistic for inter-rater agreement (particularly Kappa-Fleiss). The results obtained show that there is no agreement in detection in general, and, in those cases where a certain agreement appears, it is considered to be a fair or poor degree of agreement, according to a Kappa-Fleiss interpretation scale. This seems to confirm that there is a subjective component which makes the raters evaluate the presence of Design Smells differently. The study also raises the question of a lack of training and experience regarding Design Smells.

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Code smell detection by deep direct-learning and transfer-learning

Cited by 75 publications

References 51 publications

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

A Survey on Machine Learning Techniques for Source Code Analysis

Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

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